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Master's Dissertation
DOI
Document
Author
Full name
Alex Rocha Soares
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2019
Supervisor
Committee
Lobos, Cristian Marcelo Villegas (President)
Maia, Rafael Pimentel
Savian, Taciana Villela
Tsunemi, Miriam Harumi
Title in Portuguese
Regressão spline de nós livres para modelagem de curvas de crescimento multifásica
Keywords in Portuguese
Crescimento Multifásicos
Localidade dos nós
Número de nós
Regressão spline
Abstract in Portuguese
Neste trabalho, apresentamos os modelos de regressão spline de nós-livres como uma alternativa aos modelos não lineares utilizados em curvas de crescimento multifásico. Estudaremos o algoritmo de busca cega a través da seção dourada para escolher a melhor quantidade de nós e suas respectivas localidades. O pacote freeknotspline do software livre R foi utilizado para ajustar os modelos propostos. O critério de informação de Akaike foi usado para escolher o melhor modelo para diferentes graus do polinômio associados ao spline. Estudos de simulação foram realizados para entender melhor a posição dos nós, localidade e grau do polinômio relacionado aos modelos de regressão spline de nós-livres e como isto pode afetar a qualidade de ajuste do modelo. Com base no nosso estudo de simulação, propomos uma forma empírica de determinar o numero de nós, deixando que o algoritmo de busca escolha a posição dos nós. A metodologia é aplicada aos dados de crescimento multifásico de vacas da raça Hereford.
Title in English
Free-Knot Spline Regression for Modeling Multiphase Growth Curve
Keywords in English
Knot location
Multiphasic Growth
Number of Knot
Spline Regression
Abstract in English
In this work, we present the free-knot spline regression models as an alternative to the nonlinear models used in multiphase growth curves. We will study the blind search algorithm through the gold section to choose the best number of knot and their respective locations. The package freeknotspline of the free software R was used to fit the proposed models. The Akaike information criterion was used to choose the best model for different degrees of the polynomial associated with the spline. Simulation studies were performed to better understand the position of the knot, location and degree of the polynomial related to spline regression models of free-knots, and how this may affect the goodness of fit of the model. Based on our simulation study, we propose an empirical way of determining the number of knots, letting the search algorithm choose the position of the knots. The methodology is applied to the multiphase growth data of Hereford breed females.
 
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Publishing Date
2019-05-20
 
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