The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Richard Huggins - One of the best experts on this subject based on the ideXlab platform.
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the vgam package for capture recapture data using the conditional likelihood
Journal of Statistical Software, 2015Co-Authors: Thomas W Yee, Jakub Stoklosa, Richard HugginsAbstract:It is well known that using individual covariate information (such as body weight or gender) to model heterogeneity in capture-recapture (CR) experiments can greatly enhance inferences on the size of a Closed Population. Since individual covariates are only observable for captured individuals, complex conditional likelihood methods are usually required and these do not constitute a standard generalized linear model (GLM) family. Modern statistical techniques such as generalized additive models (GAMs), which allow a relaxing of the linearity assumptions on the covariates, are readily available for many standard GLM families. Fortunately, a natural statistical framework for maximizing conditional likelihoods is available in the Vector GLM and Vector GAM classes of models. We present several new R functions (implemented within the VGAM package) specifically developed to allow the incorporation of individual covariates in the analysis of Closed Population CR data using a GLM/GAM-like approach and the conditional likelihood. As a result, a wide variety of practical tools are now readily available in the VGAM object oriented framework. We discuss and demonstrate their advantages, features and flexibility using the new VGAM CR functions on several examples.
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A robust P-spline approach to Closed Population capture-recapture models with time dependence and heterogeneity
Computational Statistics & Data Analysis, 2012Co-Authors: Jakub Stoklosa, Richard HugginsAbstract:We extend the conditional likelihood approach to the analysis of capture-recapture experiments for Closed Populations by nonparametrically modeling the relationship between capture probabilities and individual covariates using P-splines. The model allows nonparametric functions of multivariate continuous covariates as well as categorical covariates and time effects, greatly enhancing the techniques available to an analyst. To implement this approach in practice, we found it necessary to develop a robust modification of the Horvitz-Thompson estimator. The method is illustrated on several data sets and a small simulation study is conducted.
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ASYMPTOTIC PROPERTIES OF AN OPTIMAL ESTIMATING FUNCTION APPROACH TO THE ANALYSIS OF MARK RECAPTURE DATA
Communications in Statistics - Theory and Methods, 2002Co-Authors: Richard Huggins, Anne ChaoAbstract:ABSTRACT A drawback of non parametric estimators of the size of a Closed Population in the presence of heterogeneous capture probabilities has been their lack of analytic tractability. Here we show that the martingale estimating function/sample coverage approach to estimating the size of a Closed Population with heterogeneous capture probabilities is mathematically tractable and develop its large sample properties.
Jakub Stoklosa - One of the best experts on this subject based on the ideXlab platform.
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the vgam package for capture recapture data using the conditional likelihood
Journal of Statistical Software, 2015Co-Authors: Thomas W Yee, Jakub Stoklosa, Richard HugginsAbstract:It is well known that using individual covariate information (such as body weight or gender) to model heterogeneity in capture-recapture (CR) experiments can greatly enhance inferences on the size of a Closed Population. Since individual covariates are only observable for captured individuals, complex conditional likelihood methods are usually required and these do not constitute a standard generalized linear model (GLM) family. Modern statistical techniques such as generalized additive models (GAMs), which allow a relaxing of the linearity assumptions on the covariates, are readily available for many standard GLM families. Fortunately, a natural statistical framework for maximizing conditional likelihoods is available in the Vector GLM and Vector GAM classes of models. We present several new R functions (implemented within the VGAM package) specifically developed to allow the incorporation of individual covariates in the analysis of Closed Population CR data using a GLM/GAM-like approach and the conditional likelihood. As a result, a wide variety of practical tools are now readily available in the VGAM object oriented framework. We discuss and demonstrate their advantages, features and flexibility using the new VGAM CR functions on several examples.
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A robust P-spline approach to Closed Population capture-recapture models with time dependence and heterogeneity
Computational Statistics & Data Analysis, 2012Co-Authors: Jakub Stoklosa, Richard HugginsAbstract:We extend the conditional likelihood approach to the analysis of capture-recapture experiments for Closed Populations by nonparametrically modeling the relationship between capture probabilities and individual covariates using P-splines. The model allows nonparametric functions of multivariate continuous covariates as well as categorical covariates and time effects, greatly enhancing the techniques available to an analyst. To implement this approach in practice, we found it necessary to develop a robust modification of the Horvitz-Thompson estimator. The method is illustrated on several data sets and a small simulation study is conducted.
C. Feh - One of the best experts on this subject based on the ideXlab platform.
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Demography of a socially natural herd of Przewalski's horses: an example of a small, Closed Population
Journal of Zoology, 2009Co-Authors: Laurent Tatin, Sarah R. B. King, B. Munkhtuya, A. J. M. Hewison, C. FehAbstract:Owing to habitat loss and fragmentation, large mammal Populations all over the world are becoming increasingly small and isolated. It is therefore a conservation priority to understand mechanisms influencing the demography of such Populations, which can easily be driven to extinction. The Przewalski's horse Equus ferus przewalskii remains one of the world's most endangered species and reintroduced animals are still vulnerable. Over 9 years, we analysed factors affecting mortality and female fecundity at the individual level in a predator-free, Closed Population of Przewalski's horses, which grew from 11 to 55 individuals. Similar to other wild equids, the annual growth rate of the Population was r=0.169. Typically, adult mortality was much lower than juvenile mortality, the latter being correlated with neither inbreeding coefficient of foals nor Population density. We found no link between female fecundity and operational sex ratio of the herd, or inbreeding coefficient, lactation status and body condition of the mares. Although food therefore seemed not to be limiting in this Population, density (number of horses ha−1) clearly reduced fecundity, especially in subadult mares. Thus, our results show that space can slow the growth rate of a Population before resources become limited, a potential source of concern for increasingly shrinking habitats of endangered large mammals. Possible mechanisms causing this may be found in incest avoidance or other social parameters. Finally, in large herbivores, Population density is said to exert influence in a sequential order: juvenile survival first, followed by fecundity of young females, then adult females, and adult survival last. Although we observed no link between density and juvenile survival in the studied Population, our results otherwise support this hypothesis.
Sujit K. Ghosh - One of the best experts on this subject based on the ideXlab platform.
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A Comparative Study of Bayes Estimators of Closed Population Size from Capture-Recapture Data
Journal of Statistical Theory and Practice, 2011Co-Authors: R. M. Gosky, Sujit K. GhoshAbstract:Abstract Capture-Recapture models estimate the unknown sizes of animal Populations. For Closed Populations, with constant size N during the study, eight standard models exist for estimating N. These models allow for variation in animal capture probabilities due to time effects, heterogeneity among animals, and behavioral effects after the first capture. We present Bayesian versions of these eight models. Through simulation, we compare performance of estimation of N under each model. Each model is fit to data sets generated under each of the eight model assumptions, allowing assessment of model robustness. In our simulation conditions, the most robust model in estimating N is found to be the model with behavioral and time effects. Finally, we illustrate our methods by applying them to a Population of cottontail rabbits.
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A Comparative Study of Bayesian Model Selection Criteria for Capture-Recapture Models for Closed Populations
Journal of Modern Applied Statistical Methods, 2009Co-Authors: R. M. Gosky, Sujit K. GhoshAbstract:Capture-Recapture models estimate unknown Population sizes. Eight standard Closed Population models exist, allowing for time, behavioral, and heterogeneity effects. Bayesian versions of these models are presented and use of Akaike\u27s Information Criterion (AIC) and the Deviance Information Criterion (DIC) are explored as model selection tools, through simulation and real dataset analysis
Murray G Efford - One of the best experts on this subject based on the ideXlab platform.
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density estimation in live trapping studies
Oikos, 2004Co-Authors: Murray G EffordAbstract:Unbiased estimation of Population density is a major and unsolved problem in animal trapping studies. This paper describes a new and general method for estimating density from Closed-Population capture-recapture data. Many estimators exist for the size (N) and mean capture probability (p) of a Closed Population. These statistics suffer from an unknown bias due to edge effect that varies with trap layout and home range size. The mean distance between successive captures of an individual (d) provides information on the scale of individual movements, but is itself a function of trap spacing and grid size. Our aim is to define and estimate parameters that do not depend on the trap layout. In the new method, simulation and inverse prediction are used to estimate jointly the Population density (D) and two parameters of individual capture probability, magnitude (go) and spatial scale (σ), from the information in N, p and d. The method uses any configuration of traps (e.g. grid, web or line) and any choice of Closed-Population estimator. It is assumed that home ranges have a stationary distribution in two dimensions, and that capture events may be simulated as the outcome of competing Poisson processes in time. The method is applied to simulated and field data. The estimator appears unusually robust and free from bias.