Estimação clássica e bayesiana para relação espécieárea com distribuições truncadas no zero

• Autor(a) principal: Arrabal, Claude Thiago
• Data de Publicação: 2012
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/4562
• topic: Estatística; Inferência bayesiana; Relações espécie-área; Modelos não-lineares (Estatística); Variáveis latentes; Distribuições zero-truncadas; dados de contagem; Gibbs Sampling; Metropolis-Hastings.; Zero-truncated distribution; Specie-area relationship; Non-linear models, Count data, Latente variables, Gibbs Sampling; CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA

In ecology, understanding the species-area relationship (SARs) are extremely important to determine species diversity. SARs are fundamental to assess the impact due to the destruction of natural habitats, creation of biodiversity maps, to determine the minimum area to preserve. In this study, the number of species is observed in different area sizes. These studies are referred in the literature through nonlinear models without assuming any distribution for the data. In this situation, it only makes sense to consider areas in which the counts of species are greater than zero. As the dependent variable is a count data, we assume that this variable comes from a known distribution for discrete data positive. In this paper, we used the zero truncated Poisson distribution (ZTP) and zero truncated Negative Binomial (ZTNB) to represent the probability distribution of the random variable species diversity number. To describe the relationship between species diversity and habitat, we consider nonlinear models with asymptotic behavior: Exponencial Negativo, Weibull, Logístico, Chapman-Richards, Gompertz e Beta. In this paper, we take a Bayesian approach to fit models. With the purpose of obtain the conditional distributions, we propose the use of latent variables to implement the Gibbs sampler. Introducing a comparative study through simulated data and will consider an application to a real data set.