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Neste trabalho estudamos inferencia em modelos com erros nas variaveis considerando que as variaveis do modelo sao distribuidas de acordo com uma distribuicao da familia das distribuicoes elipticas. O estudo e desenvolvido para os modelos ultraestrutural, estrutural e funcional. Como consequencia, os resultados obtidos na literatura sob normalidade sao generalizados. No modelo estrutural, discutimos o comportamento assintotico do estimador de maxima verossimilhanca e a eficiencia assintotica relativa deste em relacao a outros estimadores. Alem disso, no modelo consistindo de duas ou mais populacoes estruturais, abordamos o problema de inferencia para os coeficientes angulares do modelo. No modelo funcional, estudamos o comportamento assintotico do estimador de maxima verossimilhanca com atencao a um modelo frequentemente usado em estudos de calibracao comparativanot availabl

A transformada de Hilbert e os espaços U.M.D.

Orientador: Sergio Antonio TozaniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação CientíficaResumo: Não informado.Abstract: Not informed.MestradoMestre em Matemátic

Influence diagnostics for linear models with first-order autoregressive elliptical errors

We introduce in this paper the class of linear models with first-order autoregressive elliptical errors. The score functions and the Fisher information matrices are derived for the parameters of interest and an iterative process is proposed for the parameter estimation. Some robustness aspects of the maximum likelihood estimates are discussed. The normal curvatures of local influence are also derived for some usual perturbation schemes whereas diagnostic graphics to assess the sensitivity of the maximum likelihood estimates are proposed. The methodology is applied to analyse the daily log excess return on the Microsoft whose empirical distributions appear to have AR(1) and heavy-tailed errors. (C) 2008 Elsevier B.V. All rights reserved.CNPqConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FAPESP, BrazilFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Influence diagnostics for linear models with first-order autoregressive elliptical errors

We introduce in this paper the class of linear models with first-order autoregressive elliptical errors. The score functions and the Fisher information matrices are derived for the parameters of interest and an iterative process is proposed for the parameter estimation. Some robustness aspects of the maximum likelihood estimates are discussed. The normal curvatures of local influence are also derived for some usual perturbation schemes whereas diagnostic graphics to assess the sensitivity of the maximum likelihood estimates are proposed. The methodology is applied to analyse the daily log excess return on the Microsoft whose empirical distributions appear to have AR(1) and heavy-tailed errors.

Hypotheses testing for structural calibration model

Calibration, Maximum likelihood, EM-algorithm, Asymptotic tests, Reliability,