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Master's Dissertation
DOI
https://doi.org/10.11606/D.11.2017.tde-26072017-105153
Document
Author
Full name
Julio Cezar Souza Vasconcelos
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2017
Supervisor
Committee
Demetrio, Clarice Garcia Borges (President)
Lima, Cesar Goncalves de
Maia, Rafael Pimentel
Paula, Gilberto Alvarenga
Title in Portuguese
Modelo linear parcial generalizado simétrico
Keywords in Portuguese
Distribuições simétricas
Modelo linear parcial generalizado simétrico
Modelos GAMLSS
Modelos generalizados semiparamétricos
Abstract in Portuguese
Neste trabalho foi proposto o modelo linear parcial generalizado simétrico, com base nos modelos lineares parciais generalizados e nos modelos lineares simétricos, em que a variável resposta segue uma distribuição que pertence à família de distribuições simétricas, considerando um preditor linear que possui uma parte paramétrica e uma não paramétrica. Algumas distribuições que pertencem a essa classe são as distribuições: Normal, t-Student, Exponencial potência, Slash e Hiperbólica, dentre outras. Uma breve revisão dos conceitos utilizados ao longo do trabalho foram apresentados, a saber: análise residual, influência local, parâmetro de suavização, spline, spline cúbico, spline cúbico natural e algoritmo backfitting, dentre outros. Além disso, é apresentada uma breve teoria dos modelos GAMLSS (modelos aditivos generalizados para posição, escala e forma). Os modelos foram ajustados utilizando o pacote gamlss disponível no software livre R. A seleção de modelos foi baseada no critério de Akaike (AIC). Finalmente, uma aplicação é apresentada com base em um conjunto de dados reais da área financeira do Chile.
Title in English
Linear Model Partial Generalized Symmetric
Keywords in English
Generalized semi-parametric models
Models GAMLSS
Symmetric distributions
Symmetric generalized partial linear model
Abstract in English
In this work we propose the symmetric generalized partial linear model, based on the generalized partial linear models and symmetric linear models, that is, the response variable follows a distribution that belongs to the symmetric distribution family, considering a linear predictor that has a parametric and a non-parametric component. Some distributions that belong to this class are distributions: Normal, t-Student, Power Exponential, Slash and Hyperbolic among others. A brief review of the concepts used throughout the work was presented, namely: residual analysis, local influence, smoothing parameter, spline, cubic spline, natural cubic spline and backfitting algorithm, among others. In addition, a brief theory of GAMLSS models is presented (generalized additive models for position, scale and shape). The models were adjusted using the package gamlss available in the free R software. The model selection was based on the Akaike criterion (AIC). Finally, an application is presented based on a set of real data from Chile's financial area.
 
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Publishing Date
2017-07-31
 
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