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A new semiparametric Weibull cure rate model: fitting different behaviors within GAMLSS

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We propose a new semiparametric Weibull cure rate model for fitting nonlinear effects of explanatory variables on the mean, scale and cure rate parameters. The regression model is based on the generalized additive models for location, scale and shape, for which any or all distribution parameters can be modeled as parametric linear and/or nonparametric smooth functions of explanatory variables. We present methods to select additive terms, model estimation and validation, where all computational codes are presented in a simple way such that any R user can fit the new model. Biases of the parameter estimates caused by models specified erroneously are investigated through Monte Carlo simulations. We illustrate the usefulness of the new model by means of two applications to real data. We provide computational codes to fit the new regression model in the R software.
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Keywords: Long-term survivors; P-spline; regression models; residual analysis; smoothing functions

Document Type: Research Article

Affiliations: 1: Universidade Tecnológica Federal do Paraná, Departamento Acadêmico de Matemática, Apucarana, Brazil 2: Universidade Federal de Santa Catarina, Departamento de Informática e Estatística, Florianópolis, Brazil 3: Instituto Agronêmico do Paraná, Departamento de Socioeconomia, Londrina, Brazil 4: Universidade Estadual de Londrina, Departamento de Estatística, Londrina, Brazil 5: Universidade Estadual de Maringá, Departamento de Estatística, Maringá, Brazil 6: Universidade Federal de Pernambuco, Departamento de Estatística, Recife, Brazil

Publication date: November 18, 2019

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