title: Flexible behavioral capture-recapture modeling.
abstract: We develop alternative strategies for building and fitting parametric capture-recapture models for closed populations which can be used to address a better understanding of behavioral patterns. In the perspective of transition models, we first rely on a conditional probability parameterization. A large subset of standard capture-recapture models can be regarded as a suitable partitioning in equivalence classes of the full set of conditional probability parameters. We exploit a regression approach combined with the use of new suitable summaries of the conditioning binary partial capture histories as a device for enlarging the scope of behavioral models and also exploring the range of all possible partitions. We show how one can easily find unconditional MLE of such models within a generalized linear model framework. We illustrate the potential of our approach with the analysis of some known datasets and a simulation study.
authors.affiliation: Dipartimento di Sanità Pubblica e Malattie Infettive -Sapienza Università di Roma (Italy).
authors.forename: Danilo
authors.initials: D
authors.lastname: Alunni Fegatelli
authors.affiliation: Dipartimento di Scienze Statistiche -Sapienza Università di Roma (Italy).
authors.forename: Luca
authors.initials: L
authors.lastname: Tardella
mesh_terms: D000465:Algorithms; D000818:Animals; D001522:Behavior, Animal / Q000502:physiology*; D001699:Biometry / Q000379:methods; D019352:Censuses; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D006801:Humans; D015233:Models, Statistical; D011156:Population Density; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity
keywords: Behavioral response; Mark-recapture; Markov models; Memory effect; Memory-related summary statistics; Population size