GAMLSSs with applications to zero inflated and hierarquical data

• Ano de defesa: 2017
• Autor(a) principal: Thomas, Gustavo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Corn growth; Count of roots; GAMLSS; Mixed GAMLSSs; Modelos mistos; Modelos para dados inflacionados de zeros; Software R; Zero inflated GAMLSSs
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/11/11134/tde-06042018-150012/

The generalized additive models for location, scale and shape (GAMLSS) developed by Rigby and Stasinopoulos (2005) are a general class of univariate regression models that do not have the response distribution restricted to the exponential family as do the generalized linear and additive models, for example. In addition, they allow all the parameters of the response variable distribution to be modeled explicitly through different sets of explanatory variables. The semiparametric subclass of GAMLSS, in particular, accepts a wide range of parametric and nonparametric terms to be included in the predictors of the parameters. Similar to the generalized linear models, the GAMLSSs link predictors to parameters through monotonic link functions, which can also change for each parameter. This dissertation describes the GAMLSSs methodology and presents two applications to data sets provenient from experiments in agronomy; exploring methods of estimation, diagnosis and comparison of these models.