Eliminação de parâmetros perturbadores em um modelo de captura-recaptura

• Autor(a) principal: Salasar, Luis Ernesto Bueno
• Data de Publicação: 2011
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/4485
• topic: Estatística; Estimativas de máxima verossimilhança; Tamanho populacional; População fechada; Captura-recaptura; Eliminação de parâmetros perturbadores; Intervalos de confiança; Função de verossimilhança condicional; Função de verossimilhança integrada; Função de verossimilhança perfilada; Capture-recapture; Closed population; Conditional likelihood function; Confidence intervals; Elimination of nuisance parameters; Integrated likelihood function; Maximum likelihood estimation; Profile likelihood function; Population size; CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA

The capture-recapture process, largely used in the estimation of the number of elements of animal population, is also applied to other branches of knowledge like Epidemiology, Linguistics, Software reliability, Ecology, among others. One of the _rst applications of this method was done by Laplace in 1783, with aim at estimate the number of inhabitants of France. Later, Carl G. J. Petersen in 1889 and Lincoln in 1930 applied the same estimator in the context of animal populations. This estimator has being known in literature as _Lincoln-Petersen_ estimator. In the mid-twentieth century several researchers dedicated themselves to the formulation of statistical models appropriated for the estimation of population size, which caused a substantial increase in the amount of theoretical and applied works on the subject. The capture-recapture models are constructed under certain assumptions relating to the population, the sampling procedure and the experimental conditions. The main assumption that distinguishes models concerns the change in the number of individuals in the population during the period of the experiment. Models that allow for births, deaths or migration are called open population models, while models that does not allow for these events to occur are called closed population models. In this work, the goal is to characterize likelihood functions obtained by applying methods of elimination of nuissance parameters in the case of closed population models. Based on these likelihood functions, we discuss methods for point and interval estimation of the population size. The estimation methods are illustrated on a real data-set and their frequentist properties are analised via Monte Carlo simulation.