Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models

Bibliographic Details
Main Author: Akanda, Md. Abdus Salam
Publication Date: 2014
Other Authors: Alpizar-Jara, Russell
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10174/13864
Summary: Estimation of animal population parameters is an important issue in ecological statistics. In this paper generalized linear models (GLM), generalized additive models (GAM) and generalized estimating equations (GEE) are used to account for individual heterogeneity, modelling capture probabilities as a function of individual observed covariates. The GEE also accounts for a correlation structure among capture occasions. We are interested in estimating closed population size, where only heterogeneity is considered, there is no time e ect or behavioral response to capture, and the capture probabilities depend on covariates. A real example is used for illustrative purposes. Conditional arguments are used to obtain a Horvitz-Thompson-like estimator for estimating population size. A simulation study is also conducted to show the performance of the estimation procedure and for comparison between methodologies. The GEE approach performs better than GLM or GAM approaches for estimating population size. The simulation study highlight the importance of considering correlation among capture occasions.
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spelling Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population modelsCapture-recapture ExperimentGeneralized linear modelsGeneralized additive modelsGeneralized linear mixed modelsGeneralized estimating equationsPopulation size estimationEstimation of animal population parameters is an important issue in ecological statistics. In this paper generalized linear models (GLM), generalized additive models (GAM) and generalized estimating equations (GEE) are used to account for individual heterogeneity, modelling capture probabilities as a function of individual observed covariates. The GEE also accounts for a correlation structure among capture occasions. We are interested in estimating closed population size, where only heterogeneity is considered, there is no time e ect or behavioral response to capture, and the capture probabilities depend on covariates. A real example is used for illustrative purposes. Conditional arguments are used to obtain a Horvitz-Thompson-like estimator for estimating population size. A simulation study is also conducted to show the performance of the estimation procedure and for comparison between methodologies. The GEE approach performs better than GLM or GAM approaches for estimating population size. The simulation study highlight the importance of considering correlation among capture occasions.Sociedade Portuguesa de Estatística2015-03-31T10:43:56Z2015-03-312014-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/13864http://hdl.handle.net/10174/13864engAkanda, Md.A.S, Alpizar-Jara. (2014). Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models. In Estatística: A ciência da incerteza. Atas do XXI Congresso Anual da Sociedade Portuguesa de Estatística. (Eds. Pereira, I., Freitas, A., Scotto, M., Silva, M. E., Paulino, C. D.). Edições SPE, 169-181.978-972-8890-35-3ndalpizar@uevora.pt336Akanda, Md. Abdus SalamAlpizar-Jara, Russellinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-01-03T18:59:44Zoai:dspace.uevora.pt:10174/13864Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T12:05:45.137534Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models
title Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models
spellingShingle Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models
Akanda, Md. Abdus Salam
Capture-recapture Experiment
Generalized linear models
Generalized additive models
Generalized linear mixed models
Generalized estimating equations
Population size estimation
title_short Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models
title_full Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models
title_fullStr Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models
title_full_unstemmed Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models
title_sort Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models
author Akanda, Md. Abdus Salam
author_facet Akanda, Md. Abdus Salam
Alpizar-Jara, Russell
author_role author
author2 Alpizar-Jara, Russell
author2_role author
dc.contributor.author.fl_str_mv Akanda, Md. Abdus Salam
Alpizar-Jara, Russell
dc.subject.por.fl_str_mv Capture-recapture Experiment
Generalized linear models
Generalized additive models
Generalized linear mixed models
Generalized estimating equations
Population size estimation
topic Capture-recapture Experiment
Generalized linear models
Generalized additive models
Generalized linear mixed models
Generalized estimating equations
Population size estimation
description Estimation of animal population parameters is an important issue in ecological statistics. In this paper generalized linear models (GLM), generalized additive models (GAM) and generalized estimating equations (GEE) are used to account for individual heterogeneity, modelling capture probabilities as a function of individual observed covariates. The GEE also accounts for a correlation structure among capture occasions. We are interested in estimating closed population size, where only heterogeneity is considered, there is no time e ect or behavioral response to capture, and the capture probabilities depend on covariates. A real example is used for illustrative purposes. Conditional arguments are used to obtain a Horvitz-Thompson-like estimator for estimating population size. A simulation study is also conducted to show the performance of the estimation procedure and for comparison between methodologies. The GEE approach performs better than GLM or GAM approaches for estimating population size. The simulation study highlight the importance of considering correlation among capture occasions.
publishDate 2014
dc.date.none.fl_str_mv 2014-12-01T00:00:00Z
2015-03-31T10:43:56Z
2015-03-31
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10174/13864
http://hdl.handle.net/10174/13864
url http://hdl.handle.net/10174/13864
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Akanda, Md.A.S, Alpizar-Jara. (2014). Generalized linear models, generalized additive models and generalized estimating equations to capture-recapture closed population models. In Estatística: A ciência da incerteza. Atas do XXI Congresso Anual da Sociedade Portuguesa de Estatística. (Eds. Pereira, I., Freitas, A., Scotto, M., Silva, M. E., Paulino, C. D.). Edições SPE, 169-181.
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336
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dc.publisher.none.fl_str_mv Sociedade Portuguesa de Estatística
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dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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