Model selection criteria for overdispersed data and their application to the characterization of a host-parasite relationship
详细信息 查看全文
- 作者：Hyun-Joo Kim (1)
Joseph E. Cavanaugh (2)
Tad A. Dallas (3)
Stephanie A. Foré (4) - 关键词：AIC ; KIC ; Model selection criterion ; Overdispersion ; Quasi likelihood ; QAIC
- 刊名：Environmental and Ecological Statistics
- 出版年：2014
- 出版时间：June 2014
- 年：2014
- 卷：21
- 期：2
- 页码：329-350
- 全文大小：
- 参考文献：1. Agresti A (2002) Categorical data analysis, 2nd edn. Wiley, Hoboken CrossRef
2. Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csáki F (eds) 2nd international symposium on information theory. Akadémia Kiadó, Budapest, Hungary, pp 267-81
3. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control AC 19:716-23 CrossRef
4. American Society of Mammalogists (1998) Guidelines for the capture, handling, and care of mammals as approved by the American Society of Mammalogists. J Mammal 79(4):1416-431
5. Anderson DR, Burnham KP, White G (1994) Akaike information criterion model selection in overdispersed capture-recapture data. Ecol Soc Am 75:1780-793
6. Anderson RM, May RM (1978) Regulation and stability of host-parasite population interactions. J Animal Ecol 47:219-67 CrossRef
7. Brunner JL, Ostfeld RS (2008) Multiple causes of variable tick burdens on small-mammal hosts. Ecology 89(8):2259-272 CrossRef
8. Burnham KP, Anderson DR (2002) Model selection and multimodel inference. Springer, New York
9. Cavanaugh JE (1999) A large-sample model selection criterion based on Kullback’s symmetric divergence. Stat Probab Lett 42:333-43 CrossRef
10. Cavanaugh JE (2004) Criteria for linear model selection based on Kullback’s symmetric divergence. Aust N Z J Stat 46:257-74 CrossRef
11. Dallas T, Foré SA, Kim H-J (2012) Modeling the influence of / Peromyscus leucopus body mass, sex, and habitat on immature / Dermacentor variabilis burden. J Vector Ecol 37(2):338-41
12. Dukes JC, Rodriquez J (1976) A bioassay for host-seeking responses of tick nymphs (ixodidae). J Kansas Entomol Soc 49(4):562-66
13. Eberhardt LL (1978) Appraising variability in population studies. J Wildl Manag 42:207-38 CrossRef
14. Gallivan G, Horak I (1997) Body size and habitat as determinants of tick infestations of wild ungulates in south Africa. S Afr J Wildl Res 27(2):63-0
15. Harlan H, Foster WA (1990) Micrometeorologic factors affecting field host-seeking activity of adult Dermacentor variabilis (acari:ixodidae). J Med Entomol 27(4):471-79
16. Hilbe JM (2008) Negative binomial regression. Cambridge University Press, UK
17. Hurvich CM, Tsai CL (1989) Regression and time series model selection in small samples. Biometrika 76:297-07 CrossRef
18. Hurvich CM, Tsai CL (1995) Model selection for extended quasi-likelihood models in small samples. Biometrika 51:1077-084 CrossRef
19. Hurvich CM, Shumway RH, Tsai CL (1990) Improved estimators of Kullback-Leibler information for autoregressive model selection in small samples. Biometrika 77:297-07
20. Johnson J, Omland K (2004) Model selection in ecology and evolution. TRENDS Ecol Evol 19(2):101-08 CrossRef
21. Kim H-J, Cavanaugh JE (2005) Model selection criteria based on Kullback information measures for nonlinear regression. J Stat Plan Inference 134(2):332-49 CrossRef
22. Klein S (2000) The effects of hormones on sex differences in infection: from genes to behavior. Neurosci Biobehav Rev 24(6):627-38 CrossRef
23. Kollars T (1996) Interspecific differences between small mammals as hosts of immature Dermacentor variabilis (Acari: Ixodidae) and a model for detection of high risk areas of rocky mountain spotted fever. J Parasitol 82(5):707-10 CrossRef
24. Krasnov BR, Morand S, Hawlena H, Khokhlova IS, Shenbrot GI (2005) Sex-biased parasitism, seasonality, and sexual size dimorphism in desert rodents. Oecologia 146(2):209-17 CrossRef
25. Kullback S (1968) Information theory and statistics. Dover, Mineola
26. Lebreton J, Burnham K, Clobert J, Anderson D (1992) Model survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecol Monogr 62(1):67-18 CrossRef
27. Meinshausen N (2007) Relaxed Lasso. Comput Stat Data Anal 52:374-93 CrossRef
28. Min A, Holzmann H, Czado C (2010) Model selection strategies for identifying most relevant covariates in homoscedastic linear model. Comput Stat Data Anal 54(12):3194-211 CrossRef
29. Mooring M, Benjamin J, Harte C, Herzog N (2000) Testing the interspecific body size principle in ungulates: the smaller they come, the harder they groom. Animal Behav 60(1):35-5 CrossRef
30. Musante AR, Pekins PJ, Scarpitti DL (2007) Metabolic impacts of winter tick infestations on calf moose. Alces 43:101-07
31. Perkins SE, Cattadori IM, Tagliapietra V, Rizzoli AP, Hudson PJ (2003) Empirical evidence for key hosts in persistence of a tick-borne disease. Int J Parasitol 33:909-17 CrossRef
32. R Development Core Team (2009) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria
33. Schalk G, Forbes MR (1997) Male biases in parasitism of mammals: effects of study type, host age, and parasite taxon. Oikos 78:67-9 CrossRef
34. Shaw D, Grenfell B, Dobson A (1998) Patterns of macroparasite aggregation in wildlife host populations. Parasitology 117:597-10 CrossRef
35. Sonenshine D (1991) The biology of ticks. Oxford University Press, New York
36. Sugiura N (1978) Further analysis of the data by Akaike’s information criterion and the finite corrections. Commun Stat A7:13-6 CrossRef
37. Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc B 58:267-88
38. Wang H, Leng C (2008) A note on adaptive group Lasso. Comput Stat Data Anal 52:5277-286 CrossRef
39. Wedderburn RWM (1974) Quasilikelihood functions, generalized linear models and the Gauss-Newton method. Biometrika 61:43-7
40. Wilson K, Bjornstad O, Dobson A, Merler S, Poglayen G, Randolph S, Read A, Skorping A (2002) Heterogeneities in macroparasite infections: patterns and processes. In: Hudson PJ, Rizzoli A, Grenfell BT, Heesterbeek H, Dobson AP (eds) The ecology of wildlife diseases. Oxford University Press, New York
41. Woolhouse M, Dye C, Etard J, Smith T, Charlwood J, Garnett G, Hagan P, Hii J, Ndhlovu P, Quinnell R, Watts C, Chandiwana S, Anderson R (1997) Heterogeneities in the transmission of infectious agents: implications for the design of control programs. Natl Acad Sci USA 94:338-42 CrossRef
42. Zuk M, McKean KA (1996) Sex differences in parasite infections: patterns and processes. Int J Parasitol 26(10):1009-024 CrossRef - 作者单位：Hyun-Joo Kim (1)
Joseph E. Cavanaugh (2)
Tad A. Dallas (3)
Stephanie A. Foré (4)
1. Department of Statistics, Truman State University, Kirksville, MO, 63501, USA
2. Department of Biostatistics, The University of Iowa, Iowa city, IA, 52242, USA
3. Odum School of Ecology, The University of Georgia, Athens, GA, 30602, USA
4. Department of Biology, Truman State University, Kirksville, MO, 63501, USA - ISSN：1573-3009
文摘
In the statistical modeling of a biological or ecological phenomenon, selecting an optimal model among a collection of candidates is a critical issue. To identify an optimal candidate model, a number of model selection criteria have been developed and investigated based on estimating Kullback’s (Information theory and statistics. Dover, Mineola, 1968) directed or symmetric divergence. Criteria that target the directed divergence include the Akaike (2nd international symposium on information theory. Akadémia Kiadó, Budapest, Hungary, pp 267-81, 1973, IEEE Trans Autom Control AC 19:716-23, 1974) information criterion, AIC, and the “corrected-Akaike information criterion (Hurvich and Tsai in Biometrika 76:297-07, 1989), AICc; criteria that target the symmetric divergence include the Kullback information criterion, KIC, and the “corrected-Kullback information criterion, KICc (Cavanaugh in Stat Probab Lett 42:333-43, 1999; Aust N Z J Stat 46:257-74, 2004). For overdispersed count data, simple modifications of AIC and AICc have been increasingly utilized: specifically, the quasi Akaike information criterion, QAIC, and its corrected version, QAICc (Lebreton et al. in Ecol Monogr 62(1):67-18 1992). In this paper, we propose analogues of QAIC and QAICc based on estimating the symmetric as opposed to the directed divergence: QKIC and QKICc. We evaluate the selection performance of AIC, AICc, QAIC, QAICc, KIC, KICc, QKIC, and QKICc in a simulation study, and illustrate their practical utility in an ecological application. In our application, we use the criteria to formulate statistical models of the tick (Dermacentor variabilis) load on a white-footed mouse (Peromyscus leucopus) in northern Missouri.