forked from dvalverdem/short-course
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcourse.bib
3425 lines (2877 loc) · 234 KB
/
course.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
% This file was created with JabRef 2.10.
% Encoding: UTF8
@InCollection{Dennis2004,
title = {Statistics and the Scientific Method in Ecology},
author = {Brian Dennis},
chapter = {11},
crossref = {Taper2004},
file = {:Dennis2004.pdf:PDF},
owner = {kingaa},
timestamp = {2014.10.31}
}
@Book{Albert2007,
title = {Bayesian Computation with R},
author = {Jim Albert},
publisher = {Springer},
year = {2007},
address = {New York},
note = {ISBN 978-0-387-71384-7},
abstract = {Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples.},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springer.com/978-0-387-71384-7},
timestamp = {2007.12.31}
}
@Article{Anderson2002,
title = {Avoiding Pitfalls When Using Information-Theoretic Methods},
author = {D. R. Anderson and K. P. Burnham},
journal = {Journal of Wildlife Management},
year = {2002},
pages = {912-918},
volume = {66},
owner = {kingaa},
timestamp = {2008.01.06}
}
@Article{andrieu10,
title = {{P}article {M}arkov chain {M}onte {C}arlo methods},
author = {Andrieu, Christophe and Doucet, Arnaud and Holenstein, Roman},
journal = {Journal of the Royal Statistical Society, Series B},
year = {2010},
number = {3},
pages = {269--342},
volume = {72},
doi = {10.1111/j.1467-9868.2009.00736.x},
owner = {kingaa},
timestamp = {2016.04.15}
}
@Article{Andrieu2010,
title = {{P}article {M}arkov chain {M}onte {C}arlo methods},
author = {Andrieu, Christophe and Doucet, Arnaud and Holenstein, Roman},
journal = {Journal of the Royal Statistical Society, Series B},
year = {2010},
number = {3},
pages = {269--342},
volume = {72},
abstract = {Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two main tools to sample from high dimensional probability distributions. Although asymptotic convergence of Markov chain Monte Carlo algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. We show here how it is possible to build efficient high dimensional proposal distributions by using sequential Monte Carlo methods. This allows us not only to improve over standard Markov chain Monte Carlo schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so. We demonstrate these algorithms on a non-linear state space model and a Levy-driven stochastic volatility model.},
doi = {10.1111/j.1467-9868.2009.00736.x},
file = {Andrieu2010.pdf:Andrieu2010.pdf:PDF},
owner = {kingaa},
timestamp = {2010.06.30}
}
@Article{Anonymous1978,
title = {{I}nfluenza in a boarding school},
author = {Anonymous},
journal = {British medical journal},
year = {1978},
pages = {587},
volume = {1},
file = {Anonymous1978.pdf:Anonymous1978.pdf:PDF},
owner = {kingaa},
timestamp = {2008.07.11}
}
@Article{anonymous78,
title = {{I}nfluenza in a boarding school},
author = {Anonymous},
journal = {British medical journal},
year = {1978},
pages = {587},
volume = {1},
owner = {kingaa},
timestamp = {2016.04.15}
}
@Article{Arulampalam2002,
title = {A tutorial on particle filters for online nonlinear, non-{G}aussian {B}ayesian tracking},
author = {Arulampalam, M. S. and Maskell, S. and Gordon, N. and Clapp, T.},
journal = {IEEE Transactions on Signal Processing},
year = {2002},
pages = {174--188},
volume = {50},
doi = {10.1109/78.978374},
file = {Arulampalam2002.pdf:Arulampalam2002.pdf:PDF},
owner = {kingaa},
timestamp = {2011.07.28}
}
@Book{Becker1988,
title = {The New {S} Language},
author = {Richard A. Becker and John M. Chambers and Allan R. Wilks},
publisher = {Chapman \& Hall},
year = {1988},
address = {London},
abstract = {This book is often called the ``\emph{Blue Book}'', and introduced what is now known as S version 2.},
owner = {kingaa},
timestamp = {2007.12.31}
}
@Book{Behr2005,
title = {Einf\"uhrung in die Statistik mit {R}},
author = {Andreas Behr},
publisher = {Vahlen},
year = {2005},
address = {M\"unchen},
note = {ISBN 3-8006-3219-5, in German},
series = {WiSo Kurzlehrb\"ucher},
isbn = {3-8006-3219-5},
language = {de},
owner = {kingaa},
timestamp = {2007.12.31}
}
@InCollection{bengtsson08,
title = {Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems},
author = {Bengtsson, T. and Bickel, P. and Li, B.},
booktitle = {Probability and Statistics: Essays in Honor of {D}avid {A}. {F}reedman},
publisher = {Institute of Mathematical Statistics},
year = {2008},
address = {Beachwood, OH},
editor = {Speed, T. and Nolan, D.},
pages = {316--334},
doi = {10.1007/978-3-642-01094-1},
owner = {kingaa},
timestamp = {2016.06.08},
url = {http://arxiv.org/abs/0805.3034}
}
@Article{bhadra11,
title = {Malaria in {N}orthwest {I}ndia: Data analysis via partially observed stochastic differential equation models driven by {L}\'{e}vy noise},
author = {Bhadra,Anindya and Ionides, Edward L. and Laneri, Karina and Pascual, Mercedes and Bouma, Menno and Dhiman, R.},
journal = {Journal of the American Statistical Association},
year = {2011},
pages = {440--451},
volume = {106},
doi = {10.1198/jasa.2011.ap10323}
}
@Article{Bjornstad2001,
title = {{N}oisy clockwork: {T}ime series analysis of population fluctuations in animals},
author = {O. N. Bj{\o}rnstad and B. T. Grenfell},
journal = {Science},
year = {2001},
pages = {638--643},
volume = {293},
abstract = {Both biotic interactions and abiotic random forcing are crucial influences on population dynamics. This frequently leads to roughly equal importance of deterministic and stochastic forces. The resulting tension between noise and determinism makes ecological dynamics unique, with conceptual and methodological challenges distinctive from those in other dynamical systems. The theory for stochastic, nonlinear ecological dynamics has been developed alongside methods to test models. A range of dynamical components has been considered-density dependence, environmental and demographic stochasticity, and climatic forcing-as welt as their often complex interactions. We discuss recent advances in understanding ecological dynamics and testing theory using long-term data and review how dynamical forces interact to generate some central field and laboratory time series. SCIENCE},
doi = {10.1126/science.1062226},
file = {Bjornstad2001.pdf:Bjornstad2001.pdf:PDF},
owner = {kingaa},
timestamp = {2009.09.22}
}
@Article{Blake2014,
title = {The role of older children and adults in wild poliovirus transmission.},
author = {Blake, Isobel M. and Martin, Rebecca and Goel, Ajay and Khetsuriani, Nino and Everts, Johannes and Wolff, Christopher and Wassilak, Steven and Aylward, R Bruce and Grassly, Nicholas C.},
journal = {Proceedings of the National Academy of Sciences of the U.S.A.},
year = {2014},
month = jul,
number = {29},
pages = {10604--10609},
volume = {111},
__markedentry = {[kingaa:]},
abstract = {As polio eradication inches closer, the absence of poliovirus circulation in most of the world and imperfect vaccination coverage are resulting in immunity gaps and polio outbreaks affecting adults. Furthermore, imperfect, waning intestinal immunity among older children and adults permits reinfection and poliovirus shedding, prompting calls to extend the age range of vaccination campaigns even in the absence of cases in these age groups. The success of such a strategy depends on the contribution to poliovirus transmission by older ages, which has not previously been estimated. We fit a mathematical model of poliovirus transmission to time series data from two large outbreaks that affected adults (Tajikistan 2010, Republic of Congo 2010) using maximum-likelihood estimation based on iterated particle-filtering methods. In Tajikistan, the contribution of unvaccinated older children and adults to transmission was minimal despite a significant number of cases in these age groups [reproduction number, R = 0.46 (95\% confidence interval, 0.42-0.52) for >5-y-olds compared to 2.18 (2.06-2.45) for 0- to 5-y-olds]. In contrast, in the Republic of Congo, the contribution of older children and adults was significant [R = 1.85 (1.83-4.00)], perhaps reflecting sanitary and socioeconomic variables favoring efficient virus transmission. In neither setting was there evidence for a significant role of imperfect intestinal immunity in the transmission of poliovirus. Bringing the immunization response to the Tajikistan outbreak forward by 2 wk would have prevented an additional 130 cases (21\%), highlighting the importance of early outbreak detection and response.},
doi = {10.1073/pnas.1323688111},
file = {Blake2014.pdf:Blake2014.pdf:PDF},
institution = {Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, United Kingdom;},
keywords = {Adolescent; Adult; Age Distribution; Child; Child, Preschool; Congo, epidemiology; Disease Outbreaks, statistics /&/ numerical data; Geography; Humans; Infant; Infant, Newborn; Models, Biological; Poliomyelitis, epidemiology/transmission/virology; Poliovirus, physiology; Tajikistan, epidemiology},
language = {eng},
owner = {kingaa},
pmid = {25002465},
timestamp = {2014.10.10}
}
@Book{Boland2007,
title = {Statistical and Probabilistic Methods in Actuarial Science},
author = {Philip J. Boland},
publisher = {Chapman \& Hall/CRC},
year = {2007},
address = {Boca Raton, FL},
note = {ISBN 1-584-88695-1},
abstract = {This book covers many of the diverse methods in applied probability and statistics for students aspiring to careers in insurance, actuarial science, and finance. It presents an accessible, sound foundation in both the theory and applications of actuarial science. It encourages students to use the statistical software package R to check examples and solve problems.},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C6951},
timestamp = {2007.12.31}
}
@Article{Bolker2009a,
title = {{L}earning hierarchical models: advice for the rest of us},
author = {Bolker, Ben},
journal = {Ecological Applications},
year = {2009},
month = apr,
number = {3},
pages = {588--592},
volume = {19},
doi = {10.1890/08-0639.1},
file = {Bolker2009a.pdf:Bolker2009a.pdf:PDF},
owner = {kingaa},
timestamp = {2009.12.10}
}
@Manual{bbmle,
title = {bbmle: Tools for general maximum likelihood estimation},
author = {Bolker, Ben and {{R Development Core Team}}},
note = {R package version 1.0.17},
year = {2014},
url = {http://CRAN.R-project.org/package=bbmle}
}
@Book{Bolker2008,
title = {Ecological Models and Data in {R}},
author = {Benjamin M. Bolker},
publisher = {Princeton University Press},
year = {2008},
note = {ISBN: 9781400840908},
__markedentry = {[kingaa:]},
file = {Bolker2008.pdf:Bolker2008.pdf:PDF},
isbn = {0691125228},
owner = {kingaa},
timestamp = {2014.06.17},
url = {http://ms.mcmaster.ca/~bolker/emdbook/}
}
@Article{Bolker2009c,
title = {Generalized linear mixed models: a practical guide for ecology and evolution},
author = {Bolker, Benjamin M and Brooks, Mollie E and Clark, Connie J and Geange, Shane W and Poulsen, John R and Stevens, M Henry H and White, Jada-Simone S},
journal = {Trends in Ecology \& Evolution},
year = {2009},
number = {3},
pages = {127--135},
volume = {24},
__markedentry = {[kingaa:]},
doi = {10.1016/j.tree.2008.10.008},
file = {:Bolker2009c.pdf:PDF},
owner = {kingaa},
publisher = {Elsevier},
timestamp = {2014.11.25}
}
@Book{Braun2007,
title = {A First Course in Statistical Programming with R},
author = {W. John Braun and Duncan J. Murdoch},
publisher = {Cambridge University Press},
year = {2007},
address = {Cambridge},
note = {ISBN 978-0521872652},
abstract = {This book introduces students to statistical programming, using R as a basis. Unlike other introductory books on the R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects.},
owner = {kingaa},
pages = {362},
publisherurl = {http://www.cambridge.org/us/catalogue/catalogue.asp?isbn=9780521872652},
timestamp = {2007.12.31},
url = {http://www.stats.uwo.ca/faculty/braun/statprog/}
}
@Article{Breto2014,
title = {On idiosyncratic stochasticity of financial leverage effects},
author = {Bret{\'o}, Carles},
journal = {Statistics and Probability Letters},
year = {2014},
month = aug,
number = {0},
pages = {20--26},
volume = {91},
__markedentry = {[kingaa:]},
doi = {10.1016/j.spl.2014.04.003},
file = {:Breto2014.pdf:PDF},
keywords = {Stochastic leverage, Random-walk time-varying parameter, Non-linear non-Gaussian state-space model, Maximum likelihood estimation, Particle filter},
owner = {kingaa},
timestamp = {2014.05.22}
}
@Article{Breto2011,
title = {Compound Markov counting processes and their applications to modeling infinitesimally over-dispersed systems},
author = {Bret{\'o}, Carles and Ionides, Edward L.},
journal = {Stochastic Processes and their Applications},
year = {2011},
month = nov,
number = {11},
pages = {2571--2591},
volume = {121},
abstract = {We propose an infinitesimal dispersion index for Markov counting processes. We show that, under standard moment existence conditions, a process is infinitesimally (over-)equi-dispersed if, and only if, it is simple (compound), i.e. it increases in jumps of one (or more) unit(s), even though infinitesimally equi-dispersed processes might be under-, equi- or over-dispersed using previously studied indices. Compound processes arise, for example, when introducing continuous-time white noise to the rates of simple processes resulting in Lévy-driven SDEs. We construct multivariate infinitesimally over-dispersed compartment models and queuing networks, suitable for applications where moment constraints inherent to simple processes do not hold.},
doi = {10.1016/j.spa.2011.07.005},
file = {Breto2011.pdf:Breto2011.pdf:PDF},
issn = {0304-4149},
keywords = {Continuous time, Counting Markov process, Birth–death process, Environmental stochasticity, Infinitesimal over-dispersion, Simultaneous events},
owner = {kingaa},
timestamp = {2012.01.17}
}
@Article{breto09,
title = {{T}ime series analysis via mechanistic models},
author = {Carles Bret\'{o} and Daihai He and Edward L. Ionides and Aaron A. King},
journal = {Annals of Applied Statistics},
year = {2009},
number = {1},
pages = {319--348},
volume = {3},
doi = {10.1214/08-AOAS201},
owner = {kingaa},
timestamp = {2016.06.08}
}
@Article{Breto2009,
title = {{T}ime series analysis via mechanistic models},
author = {Carles Bret\'{o} and Daihai He and Edward L. Ionides and Aaron A. King},
journal = {Annals of Applied Statistics},
year = {2009},
number = {1},
pages = {319--348},
volume = {3},
abstract = {The purpose of time series analysis via mechanistic models is to reconcile the known or hypothesized structure of a dynamical system with observations collected over time. We develop a framework for constructing nonlinear mechanistic models and carrying out inference. Our framework permits the consideration of implicit dynamic models, meaning statistical models for stochastic dynamical systems which are specified by a simulation algorithm to generate sample paths. Inference procedures that operate on implicit models are said to have the plug-and-play property. Our work builds on recently developed plug-and-play inference methodology for partially observed Markov models. We introduce a class of implicitly specified Markov chains with stochastic transition rates, and we demonstrate its applicability to open problems in statistical inference for biological systems. As one example, these models are shown to give a fresh perspective on measles transmission dynamics. As a second example, we present a mechanistic analysis of cholera incidence data, involving interaction between two competing strains of the pathogen Vibrio cholerae.},
doi = {10.1214/08-AOAS201},
file = {Breto2009.pdf:Breto2009.pdf:PDF;:Breto2009_supp.pdf:PDF},
owner = {kingaa},
timestamp = {2009.09.15}
}
@Article{breto11,
title = {Compound {M}arkov counting processes and their applications to modeling infinitesimally over-dispersed systems},
author = {Bret\'{o}, C. and Ionides, E. L.},
journal = {Stochastic Processes and their Applications},
year = {2011},
pages = {2571--2591},
volume = {121},
owner = {kingaa},
timestamp = {2016.06.08}
}
@Book{Burns2012,
title = {The R Inferno},
author = {Patrick Burns},
publisher = {lulu.com},
year = {2012},
edition = {2nd},
file = {Burns2012.pdf:Burns2012.pdf:PDF},
owner = {kingaa},
timestamp = {2014.09.17},
url = {http://www.burns-stat.com/pages/Tutor/R_inferno.pdf}
}
@Article{Cai2007,
title = {{K}-leap method for accelerating stochastic simulation of coupled chemical reactions},
author = {Xiaodong Cai and Zhouyi Xu},
journal = {Journal of Chemical Physics},
year = {2007},
pages = {074102},
volume = {126},
abstract = {Leap methods are very promising for accelerating stochastic simulation of a well stirred chemically reacting system, while providing acceptable simulation accuracy. In Gillespie's -leap method [D. Gillespie, J. Phys. Chem. 115, 1716 (2001)], the number of firings of each reaction channel during a leap is a Poisson random variable, whose sample values are unbounded. This may cause large changes in the populations of certain molecular species during a leap, thereby violating the leap condition. In this paper, we develop an alternative leap method called the K-leap method, in which we constrain the total number of reactions occurring during a leap to be a number K calculated from the leap condition. As the number of firings of each reaction channel during a leap is upper bounded by a properly chosen number, our K-leap method can better satisfy the leap condition, thereby improving simulation accuracy. Since the exact stochastic simulation algorithm (SSA) is a special case of our K-leap method when K=1, our K-leap method can naturally change from the exact SSA to an approximate leap method during simulation, whenever the leap condition allows to do so.},
doi = {10.1063/1.2436869 },
file = {Cai2007.pdf:Cai2007.pdf:PDF},
owner = {kingaa},
timestamp = {2007.03.28}
}
@Article{Camacho2011,
title = {Explaining rapid reinfections in multiple-wave influenza outbreaks: {T}ristan da {C}unha 1971 epidemic as a case study},
author = {Camacho, Anton and Ballesteros, S{\'e}bastien and Graham, Andrea L. and Carrat, Fabrice and Ratmann, Oliver and Cazelles, Bernard},
journal = {Proceedings of the Royal Society of London. Series B},
year = {2011},
month = apr,
pages = {--},
abstract = {Influenza usually spreads through the human population in multiple-wave outbreaks. Successive reinfection of individuals over a short time interval has been explicitly reported during past pandemics. However, the causes of rapid reinfection and the role of reinfection in driving multiple-wave outbreaks remain poorly understood. To investigate these issues, we focus on a two-wave influenza A/H3N2 epidemic that occurred on the remote island of Tristan da Cunha in 1971. Over 59 days, 273 (96%) of 284 islanders experienced at least one attack and 92 (32%) experienced two attacks. We formulate six mathematical models invoking a variety of antigenic and immunological reinfection mechanisms. Using a maximum-likelihood analysis to confront model predictions with the reported incidence time series, we demonstrate that only two mechanisms can be retained: some hosts with either a delayed or deficient humoral immune response to the primary influenza infection were reinfected by the same strain, thus initiating the second epidemic wave. Both mechanisms are supported by previous empirical studies and may arise from a combination of genetic and ecological causes. We advocate that a better understanding and account of heterogeneity in the human immune response are essential to analysis of multiple-wave influenza outbreaks and pandemic planning.},
doi = {10.1098/rspb.2011.0300},
file = {Camacho2011.pdf:Camacho2011.pdf:PDF},
owner = {kingaa},
timestamp = {2011.06.01}
}
@Article{Caswell1988,
title = {Theory and models in ecology: A different perspective},
author = {Caswell, Hal},
journal = {Ecological Modelling},
year = {1988},
month = oct,
number = {1-2},
pages = {33--44},
volume = {43},
abstract = {Many widespread criticisms of ecological theory, especially theory relying on mathematical models, are based on misunderstandings the role of models in theory and of theory in the larger discipline of ecology. The naivete of some of these misunderstandings suggests that they are too seldom examined. In this paper, I address what I consider to be the most ill-conceived criticisms of ecological theory. I propose that the parallels between theoretical and empirical research are more profound than most people realize. Appreciation of these parallels will go a long way towards bridging the gap between empiricists and theoreticians in ecology},
doi = {10.1016/0304-3800(88)90071-3},
owner = {kingaa},
timestamp = {2008.01.06}
}
@Book{Chambers2007,
title = {Software for Data Analysis: Programming with R},
author = {Chambers, John M.},
publisher = {Springer},
year = {2007},
address = {New York},
note = {ISBN 978-0-387-75935-7},
abstract = {John Chambers has been the principal designer of the S language since its beginning, and in 1999 received the ACM System Software award for S, the only statistical software to receive this award. He is author or coauthor of the landmark books on S. Now he turns to R, the enormously successful open-source system based on the S language. R's international support and the thousands of packages and other contributions have made it the standard for statistical computing in research and teaching. This book guides the reader through programming with R, from interactive use through all stages from simple functions to the design of R packages. It includes key modern enhancements such as classes and methods, namespaces and interfaces to spreadsheets and data bases.},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http:///www.springer.com/978-0-387-75935-7},
timestamp = {2007.12.31}
}
@Book{Chambers1998,
title = {Programming with Data},
author = {John M. Chambers},
publisher = {Springer},
year = {1998},
address = {New York},
note = {ISBN 0-387-98503-4},
abstract = {This ``\emph{Green Book}'' describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process.},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-2008951-0,00.html},
timestamp = {2007.12.31},
url = {http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/}
}
@Book{Chambers1992,
title = {Statistical Models in {S}},
author = {John M. Chambers and Trevor J. Hastie},
publisher = {Chapman \& Hall},
year = {1992},
address = {London},
abstract = {This is also called the ``\emph{White Book}'', and introduced S version 3, which added structures to facilitate statistical modeling in S.},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C3040&parent_id=&pc=},
timestamp = {2007.12.31}
}
@Article{Chen2004,
title = {Multiple periodic solutions of delayed predator-prey systems with type {IV} functional responses},
author = {Chen, Y.},
journal = {Nonlinear Analysis: Real World Applications},
year = {2004},
pages = {45--53},
volume = { 5},
owner = {kingaa},
timestamp = {2008.01.28}
}
@Article{Chib2005,
title = {Accept--reject Metropolis--Hastings sampling and marginal likelihood estimation},
author = {Chib, Siddhartha and Jeliazkov, Ivan},
journal = {Statistica Neerlandica},
year = {2005},
number = {1},
pages = {30--44},
volume = {59},
__markedentry = {[kingaa:]},
doi = {10.1111/j.1467-9574.2005.00277.x},
file = {:Chib2005.pdf:PDF},
owner = {kingaa},
publisher = {Wiley Online Library},
timestamp = {2014.11.18}
}
@Article{Chib2001,
title = {Marginal likelihood from the Metropolis--Hastings output},
author = {Chib, Siddhartha and Jeliazkov, Ivan},
journal = {Journal of the American Statistical Association},
year = {2001},
number = {453},
pages = {270--281},
volume = {96},
__markedentry = {[kingaa:]},
doi = {10.1198/016214501750332848},
file = {:Chib2001.pdf:PDF},
owner = {kingaa},
publisher = {Taylor \& Francis},
timestamp = {2014.11.18}
}
@Article{Christensen2014,
title = {Are female hurricanes really deadlier than male hurricanes?},
author = {Christensen, Bj{\"o}rn and Christensen, S{\"o}ren},
journal = {Proceedings of the National Academy of Sciences},
year = {2014},
number = {34},
pages = {E3497--E3498},
volume = {111},
__markedentry = {[kingaa:]},
owner = {kingaa},
publisher = {National Acad Sciences},
timestamp = {2014.11.17}
}
@Book{Clark2007,
title = {Models for ecological data : an introduction},
author = {Clark, James},
publisher = {Princeton University Press},
year = {2007},
address = {Princeton},
isbn = {9780691121789},
owner = {kingaa},
timestamp = {2014.10.31}
}
@Article{Clark2005,
title = {Why environmental scientists are becoming {B}ayesians},
author = {Clark, James S},
journal = {Ecology Letters},
year = {2005},
number = {1},
pages = {2--14},
volume = {8},
__markedentry = {[kingaa:]},
doi = {10.1111/j.1461-0248.2004.00702.x},
file = {:Clark2005.pdf:PDF},
owner = {kingaa},
publisher = {Wiley Online Library},
timestamp = {2014.10.30}
}
@Article{Clark1999,
title = {Seed dispersal near and far: patterns across temperate and tropical forests},
author = {Clark, J. S. and M. Silman and R. Kern and E. Macklin and J. HilleRisLambers},
journal = {Ecology},
year = {1999},
pages = {1475--1494},
volume = {80},
owner = {kingaa},
timestamp = {2008.02.04}
}
@Book{Cook2007,
title = {Interactive and Dynamic Graphics for Data Analysis},
author = {Dianne Cook and Deborah F. Swayne},
publisher = {Springer},
year = {2007},
address = {New York},
note = {ISBN 978-0-387-71761-6},
abstract = {This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models. All examples are based on freely available software: GGobi for interactive graphics and R for static graphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses in the book, along with movies demonstrating the examples.},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springer.com/978-0-387-71761-6},
timestamp = {2007.12.31}
}
@Book{Crawley2005,
title = {Statistics: An Introduction using R},
author = {Michael J. Crawley},
publisher = {Wiley},
year = {2005},
note = {ISBN 0-470-02297-3},
abstract = {The book is primarily aimed at undergraduate students in medicine, engineering, economics and biology --- but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R.},
owner = {kingaa},
publisherurl = {http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470022973.html},
timestamp = {2007.12.31},
url = {http://www.bio.ic.ac.uk/research/crawley/statistics/}
}
@InCollection{Crome1997,
title = {Researching tropical forest fragmentation: Shall we keep on doing what we're doing?},
author = {Francis H. J. Crome},
booktitle = {Tropical forest remnants: ecology, management, and conservation of fragmented communities},
publisher = {University of Chicago Press},
year = {1997},
address = {Chicago, IL},
chapter = {31},
editor = {Laurance, W. F. and Bierregard, R. O.},
pages = {485--501}
}
@Book{Dalgaard2002,
title = {Introductory Statistics with {R}},
author = {Peter Dalgaard},
publisher = {Springer},
year = {2002},
note = {ISBN 0-387-95475-9},
orderinfo = {springer.txt},
owner = {kingaa},
pages = {288},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-10130-22-2287329-0,00.html?changeHeader=true},
timestamp = {2007.12.31},
url = {http://www.biostat.ku.dk/~pd/ISwR.html}
}
@Article{Davidson1948,
title = {Annual trends in a natural population of {{\em {T}hrips imaginis}} ({T}hysanoptera)},
author = {Davidson, J. and Andrewartha, H. G.},
journal = {Journal of Animal Ecology},
year = {1948},
pages = {193--199},
volume = {17},
doi = {10.2307/1484},
file = {:Davidson1948.pdf:PDF},
owner = {kingaa},
timestamp = {2012.09.10}
}
@Book{Davison1997,
title = {{B}ootstrap {M}ethods and their {A}pplication},
author = {A.C. Davison and D.V. Hinkley},
publisher = {Cambridge University Press},
year = {1997},
address = {New York},
owner = {kingaa},
timestamp = {2011.05.26}
}
@Article{Dawid1973,
title = {Marginalization paradoxes in Bayesian and structural inference},
author = {Dawid, A\_P and Stone, Mervyn and Zidek, James V},
journal = {Journal of the Royal Statistical Society. Series B},
year = {1973},
pages = {189--233},
file = {:Dawid1973.pdf:PDF},
owner = {kingaa},
publisher = {JSTOR},
timestamp = {2014.10.30},
url = {http://www.jstor.org/stable/2984907}
}
@Article{Dennis1996,
title = {Should Ecologists Become {B}ayesians?},
author = {Brian Dennis},
journal = {Ecological Applications},
year = {1996},
pages = {1095--1103},
volume = {6},
abstract = {Bayesian statistics involve substantial changes in the methods and philosophy of science. Before adopting Bayesian approaches, ecologists should consider carefully whether or not scientific understanding will be enhanced. Frequentist statistical methods, while imperfect, have made an unquestioned contribution to scientific progress and are a workhorse of day-to-day research. Bayesian statistics, by contrast, have a largely untested track record. The papers in this special section on Bayesian statistics exemplify the difficulties inherent in making convincing scientific arguments with Bayesian reasoning.},
doi = {10.2307/2269594},
file = {Dennis1996.pdf:Dennis1996.pdf:PDF},
owner = {kingaa},
timestamp = {2009.09.22}
}
@Article{Dennis2006,
title = {Estimating density dependence, process noise, and observation error},
author = {Dennis, Brian and Ponciano, Jos{\'e} Miguel and Lele, Subhash R and Taper, Mark L and Staples, David F},
journal = {Ecological Monographs},
year = {2006},
number = {3},
pages = {323--341},
volume = {76},
__markedentry = {[kingaa:]},
abstract = {We describe a discrete-time, stochastic population model with density dependence, environmental-type process noise, and lognormal observation or sampling error. The model, a stochastic version of the Gompertz model, can be transformed into a linear Gaussian state-space model (Kalman filter) for convenient fitting to time series data. The model has a multivariate normal likelihood function and is simple enough for a variety of uses ranging from theoretical study of parameter estimation issues to routine data analyses in population monitoring. A special case of the model is the discrete-time, stochastic exponential growth model (density independence) with environmental-type process error and lognormal observation error. We describe two methods for estimating parameters in the Gompertz state-space model, and we compare the statistical qualities of the methods with computer simulations. The methods are maximum likelihood based on observations and restricted maximum likelihood based on first differences. Both offer adequate statistical properties. Because the likelihood function is identical to a repeated-measures analysis of variance model with a random time effect, parameter estimates can be calculated using PROC MIXED of SAS. We use the model to analyze a data set from the Breeding Bird Survey. The fitted model suggests that over 70% of the noise in the population's growth rate is due to observation error. The model describes the autocovariance properties of the data especially well. While observation error and process noise variance parameters can both be estimated from one time series, multimodal likelihood functions can and do occur. For data arising from the model, the statistically consistent parameter estimates do not necessarily correspond to the global maximum in the likelihood function. Maximization, simulation, and bootstrapping programs must accommodate the phenomenon of multimodal likelihood functions to produce statistically valid results.},
doi = {10.1890/0012-9615(2006)76%5B323:EDDPNA%5D2.0.CO%3B2},
file = {Dennis2006.pdf:Dennis2006.pdf:PDF},
owner = {kingaa},
publisher = {Eco Soc America},
timestamp = {2014.12.08}
}
@Book{Deonier2005,
title = {Computational Genome Analysis: An Introduction},
author = {Richard C. Deonier and Simon Tavar{\'e} and Michael S. Waterman},
publisher = {Springer},
year = {2005},
note = {ISBN: 0-387-98785-1},
abstract = {Computational Genome Analysis: An Introduction presents the foundations of key p roblems in computational molecular biology and bioinformatics. It focuses on com putational and statistical principles applied to genomes, and introduces the mat hematics and statistics that are crucial for understanding these applications. A ll computations are done with R.},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springeronline.com/0-387-98785-1},
timestamp = {2007.12.31}
}
@Book{Diggle2006,
title = {Model-based Geostatistics},
author = {Peter J. Diggle and Paulo Justiniano Ribeiro},
publisher = {Springer},
year = {2006},
note = {ISBN 0-387-32907-2},
abstract = {Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomena, using data obtained at a limited number of spatial locations. The name reflects its origins in mineral exploration, but the methods are now used in a wide range of settings including public health and the physical and environmental sciences. Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume is the first book-length treatment of model-based geostatistics.},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springer.com/0-387-32907-2},
timestamp = {2007.12.31}
}
@Book{Dolic2004,
title = {Statistik mit {R}. Einf\"uhrung f\"ur Wirtschafts- und Sozialwissenschaftler},
author = {Dubravko Dolic},
publisher = {R. Oldenbourg},
year = {2004},
address = {M\"unchen, Wien},
note = {ISBN 3-486-27537-2, in German},
isbn = {3-486-27537-2},
language = {de},
owner = {kingaa},
timestamp = {2007.12.31}
}
@Book{Dudoit2007,
title = {Multiple Testing Procedures and Applications to Genomics},
author = {Sandrine Dudoit and Mark J. {van der Laan}},
publisher = {Springer},
year = {2007},
note = {ISBN: 978-0-387-49316-9},
series = {Springer Series in Statistics},
abstract = {This book provides a detailed account of the theoretical foundations of proposed multiple testing methods and illustrates their application to a range of testing problems in genomics.},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springeronline.com/978-0-387-49316-9},
timestamp = {2007.12.31}
}
@Article{Duncan2000,
title = {Forest Succession and Distance from Forest Edge in an Afro-Tropical Grassland},
author = {Duncan, R Scot and Duncan, Virginia E},
journal = {Biotropica},
year = {2000},
number = {1},
pages = {33--41},
volume = {32},
__markedentry = {[kingaa:]},
abstract = {Forest succession on degraded tropical lands often is slowed by impoverished seed banks and low rates of seed dispersal. Within degraded landscapes, remnant forests are potential seed sources that could enhance nearby forest succession. The spatial extent that forest can influence succession, however, remains largely unstudied. In abandoned agricultural lands in Kibale National Park, Uganda, recurrent fires have helped perpetuate the dominance of tall (2–3 m) grasses. We examined the effects of distance from forest and grassland vegetation structure on succession in a grassland having several years of fire exclusion. At 10 and 25 m from forest edge, we quantified vegetation patterns, seed predation, and survival of planted tree seedlings. Natural vegetation was similar at both distances, as was seed (eight species) and seedling (six species) survival; however, distance may be important at spatial or temporal scales not examined in this study. Our results offer insight into forest succession on degraded tropical grasslands following fire exclusion. Naturally recruited trees and tree seedlings were scarce, and seed survival was low (20% after 7 mo). While seedling survival was high (95% after 6 to 8 mo), seedling shoot growth was very slow (x̄= 0.5 cm/100 d), suggesting that survivorship eventually may decline. Recurrent fires often impede forest succession in degraded tropical grasslands; however, even with fire exclusion, our study suggests that forest succession can be very slow, even in close proximity to forest.},
doi = {10.1111/j.1744-7429.2000.tb00445.x},
file = {:Duncan2000.pdf:PDF},
owner = {kingaa},
publisher = {Wiley Online Library},
timestamp = {2014.09.14}
}
@Article{Dwyer1990,
title = {A simulation model of the population dynamics and evolution of myxomatosis},
author = {Dwyer, Greg and Levin, Simon A and Buttel, Linda},
journal = {Ecological Monographs},
year = {1990},
month = {Dec.},
number = {4},
pages = {423--447},
volume = {60},
abstract = {Myxoma virus was released into Australia to control the introduced European rabbit, Oryctolagus cuniculus. Within a few years after introduction, the virulence of the virus had declined to an intermediate level, while the resistance of field rabbits and increased sharply. In the nearly 40 yr since the disease was introduced, host resistance has continued to increase, while viral virulence has only recently begun to show signs of counter-increases in some areas. The two questions of interest are thus: Is this system in a coevolutionary arms race (Dawkins and Krebs 1979); that is, will both host and pathogen continue to evolve antagonistically? Will the virus continue to control the rabbit in the future? We present a simulation model based loosely on previous host--pathogen models (Anderson and May 1979), but with detailed accounting of the virus titer in infected hosts, and using realistic estimates of the demographic parameters of the rabbit, including age structure and seasonally varying reproduction. For a single virulence grade, by varying the non-disease (or @'natural@') mortality of the rabbit, the age at first reproduction of the rabbit, and the virulence grade of the virus, we explored the parameter range for which the rabbit population is controlled. For the most prevalent grades of the virus, grades IIIB and IV, the virus can control the rabbit for most realistic values of natural mortality and age at first reproduction. However, control is dependent on both natural mortality and virus virulence. Since natural mortality varies both geographically and seasonally, the usefulness of the virus may vary geographically and seasonally, and management policies must be sensitive to this variation. When competing against several virus strains that together encompass the complete range of virulence seen in the field, a strain of grade IV virulence competitively excludes strains of all other grades. This competitively dominant grade is close to the most prevalent virulence grades seen in the field. We discuss possible mechanisms of coexistence, including local competitive exclusion with global persistence, variability in host resistance, high mutation rates, and trade-offs between within-host and between-host competitive ability. By examining the effects of flea transmission efficiency, we are able to show that, contrary to commonly held belief, whatever effect fleas have upon the outcome of selection on virulence cannot be due to differences in transmission efficiency between fleas and mosquitoes. Finally, by including host resistance, we improve our prediction of the most prevalent grade of virulence. We conclude that control of the rabbit by the virus is likely for the near future, but that until we understand the genetics of resistance in the rabbit and the relationship between resistance and virulence for different grades of virulence, for different grades of virulence, we cannot make a useful prediction of the long-term state of this system.},
file = {Dwyer1990.pdf:Dwyer1990.pdf:PDF},
keywords = {biological control, coevolution, disease transmission, epizootiology, myxomatosis, Oryctolagus cuniculus, population dynamics, simulation model, virulence},
owner = {kingaa},
publisher = {JSTOR},
timestamp = {2014.09.02},
url = {http://www.jstor.org/stable/1943014}
}
@Article{Eisenberg2014,
title = {Determining identifiable parameter combinations using subset profiling.},
author = {Eisenberg, Marisa C. and Hayashi, Michael A L.},
journal = {Mathematical Biosciences},
year = {2014},
month = aug,
__markedentry = {[kingaa:]},
abstract = {Identifiability is a necessary condition for successful parameter estimation of dynamic system models. A major component of identifiability analysis is determining the identifiable parameter combinations, the functional forms for the dependencies between unidentifiable parameters. Identifiable combinations can help in model reparameterization and also in determining which parameters may be experimentally measured to recover model identifiability. Several numerical approaches to determining identifiability of differential equation models have been developed, however the question of determining identifiable combinations remains incompletely addressed. In this paper, we present a new approach which uses parameter subset selection methods based on the Fisher Information Matrix, together with the profile likelihood, to effectively estimate identifiable combinations. We demonstrate this approach on several example models in pharmacokinetics, cellular biology, and physiology.},
doi = {10.1016/j.mbs.2014.08.008},
file = {:Eisenberg2014.pdf:PDF},
institution = {Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, United States. Electronic address: [email protected].},
language = {eng},
medline-pst = {aheadofprint},
owner = {kingaa},
pii = {S0025-5564(14)00163-1},
pmid = {25173434},
timestamp = {2014.09.16}
}
@Article{Ellison2004,
title = {Bayesian inference in ecology},
author = {Ellison, Aaron M},
journal = {Ecology Letters},
year = {2004},
number = {6},
pages = {509--520},
volume = {7},
doi = {10.1111/j.1461-0248.2004.00603.x},
file = {:Ellison2004.pdf:PDF},
owner = {kingaa},
publisher = {Wiley Online Library},
timestamp = {2014.10.30}
}
@Article{Ellison1996,
title = {An introduction to {B}ayesian inference for ecological research and environmental decision-making},
author = {Ellison, Aaron M},
journal = {Ecological Applications},
year = {1996},
pages = {1036--1046},
__markedentry = {[kingaa:]},
doi = {10.2307/2269588},
file = {:Ellison1996.pdf:PDF},
owner = {kingaa},
publisher = {JSTOR},
timestamp = {2014.10.30}
}
@Book{Eubank2006,
title = {A {Kalman} filter primer},
author = {Eubank, R. L.},
publisher = {Chapman \& Hall/CRC},
year = {2006},
address = {Boca Raton, Fla},
isbn = {0824723651},
owner = {kingaa},
timestamp = {2014.12.08}
}
@Book{Everitt2006,
title = {A Handbook of Statistical Analyses Using R},
author = {Brian Everitt and Torsten Hothorn},
publisher = {Chapman \& Hall/CRC},
year = {2006},
address = {Boca Raton, FL},
note = {ISBN 1-584-88539-4},
abstract = {With emphasis on the use of R and the interpretation of results rather than the theory behind the methods, this book addresses particular statistical techniques and demonstrates how they can be applied to one or more data sets using R. The authors provide a concise introduction to R, including a summary of its most important features. They cover a variety of topics, such as simple inference, generalized linear models, multilevel models, longitudinal data, cluster analysis, principal components analysis, and discriminant analysis. With numerous figures and exercises, A Handbook of Statistical Analysis using R provides useful information for students as well as statisticians and data analysts.},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C5394&parent_id=&pc=},
timestamp = {2007.12.31},
url = {http://cran.r-project.org/src/contrib/Descriptions/HSAUR.html}
}
@Book{Everitt2005,
title = {An R and S-Plus Companion to Multivariate Analysis},
author = {Brian S. Everitt},
publisher = {Springer},
year = {2005},
note = {ISBN 1-85233-882-2},
abstract = {In this book the core multivariate methodology is covered along with some basic theory for each method described. The necessary R and S-Plus code is given for each analysis in the book, with any differences between the two highlighted.},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-34953445-0,00.html},
timestamp = {2007.12.31},
url = {http://biostatistics.iop.kcl.ac.uk/publications/everitt/}
}
@Book{Faraway2006,
title = {Extending Linear Models with {R}: Generalized Linear, Mixed Effects and Nonparametric Regression Models},
author = {Julian J. Faraway},
publisher = {Chapman \& Hall/CRC},
year = {2006},
address = {Boca Raton, FL},
note = {ISBN 1-584-88424-X},
abstract = {This book surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses.},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C424X&parent_id=&pc=},
timestamp = {2007.12.31},
url = {http://www.maths.bath.ac.uk/~jjf23/ELM/}
}
@Book{Faraway2004,
title = {Linear Models with R},
author = {Julian J. Faraway},
publisher = {Chapman \& Hall/CRC},
year = {2004},
address = {Boca Raton, FL},
note = {ISBN 1-584-88425-8},
abstract = {The book focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion of topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results.},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C4258&parent_id=&pc=},
timestamp = {2007.12.31},
url = {http://www.maths.bath.ac.uk/~jjf23/LMR/}
}
@Electronic{Fasiolo2014,
title = {Statistical inference for highly non-linear dynamical models in ecology and epidemiology},
author = {{Fasiolo}, M. and {Pya}, N. and {Wood}, S.},
howpublished = {arXiv e-print 1411.4564},
url = {http://arxiv.org/abs/1411.4564},
year = {2014},
journal = {ArXiv},
keywords = {Statistics - Methodology, Statistics - Applications},
pages = {1411.4564},
primaryclass = {stat.ME}
}
@Article{Fawcett2012,
title = {Heavy use of equations impedes communication among biologists.},
author = {Fawcett, Tim W. and Higginson, Andrew D.},
journal = {Proceedings of the National Academy of Sciences of the U.S.A.},
year = {2012},
month = jul,
number = {29},
pages = {11735--11739},
volume = {109},
__markedentry = {[kingaa:]},
abstract = {Most research in biology is empirical, yet empirical studies rely fundamentally on theoretical work for generating testable predictions and interpreting observations. Despite this interdependence, many empirical studies build largely on other empirical studies with little direct reference to relevant theory, suggesting a failure of communication that may hinder scientific progress. To investigate the extent of this problem, we analyzed how the use of mathematical equations affects the scientific impact of studies in ecology and evolution. The density of equations in an article has a significant negative impact on citation rates, with papers receiving 28\% fewer citations overall for each additional equation per page in the main text. Long, equation-dense papers tend to be more frequently cited by other theoretical papers, but this increase is outweighed by a sharp drop in citations from nontheoretical papers (35\% fewer citations for each additional equation per page in the main text). In contrast, equations presented in an accompanying appendix do not lessen a paper's impact. Our analysis suggests possible strategies for enhancing the presentation of mathematical models to facilitate progress in disciplines that rely on the tight integration of theoretical and empirical work.},
doi = {10.1073/pnas.1205259109},
institution = {School of Biological Sciences, University of Bristol, Bristol BS8 1UG, United Kingdom. [email protected]},
keywords = {Biological Evolution; Communication Barriers; Ecology, methods; Information Dissemination, methods; Journal Impact Factor; Mathematics; Models, Statistical},
language = {eng},
medline-pst = {ppublish},
owner = {kingaa},
pii = {1205259109},
pmid = {22733777},
timestamp = {2014.09.23}
}
@Article{Filipe2001,
title = {Comparing approximations to spatio-temporal models for epidemics with local spread.},
author = {J. A. Filipe and G. J. Gibson},
journal = {Bulletin of Mathematical Biology},
year = {2001},
month = {Jul},
number = {4},
pages = {603--624},
volume = {63},
abstract = {Analytical methods for predicting and exploring the dynamics of stochastic, spatially interacting populations have proven to have useful application in epidemiology and ecology. An important development has been the increasing interest in spatially explicit models, which require more advanced analytical techniques than the usual mean-field or mass-action approaches. The general principle is the derivation of differential equations describing the evolution of the expected population size and other statistics. As a result of spatial interactions no closed set of equations is obtained. Nevertheless, approximate solutions are possible using closure relations for truncation. Here we review and report recent progress on closure approximations applicable to lattice models with nearest-neighbour interactions, including cluster approximations and elaborations on the pair (or pairwise) approximation. This study is made in the context of an SIS model for plant-disease epidemics introduced in Filipe and Gibson (1998, Studying and approximating spatio-temporal models for epidemic spread and control, Phil. Trans. R. Soc. Lond. B 353, 2153-2162) of which the contact process [Harris, T. E. (1974), Contact interactions on a lattice, Ann. Prob. 2, 969] is a special case. The various methods of approximation are derived and explained and their predictions are compared and tested against simulation. The merits and limitations of the various approximations are discussed. A hybrid pairwise approximation is shown to provide the best predictions of transient and long-term, stationary behaviour over the whole parameter range of the model.},
doi = {10.1006/bulm.2001.0234},
institution = { Statistics Scotland, Edinburgh, UK. [email protected]},