Atividade

94590 - Distributional regression approach using GAMLSS

Período da turma: 13/02/2020 a 14/02/2020

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Descrição: Ementa do curso: The generalized additive models for location, scale and shape, GAMLSS, are univariate distributional regression models, where all the parameters of the assumed distribution for the response variable can be modelled as additive functions of the explanatory variables. GAMLSS address the problem of choosing an appropriate distribution for the response variable, and models how the distribution parameters vary with changes in the explanatory variables. GAMLSS, its statistical modelling philosophy and its implementation in the software R will be introduced. The different distributions for modelling the response variable, and their properties will be described. These distributions include continuous (positively or negatively skewed and with high or low kurtosis), discrete and mixed distributions. Different additive terms for modelling the parameters of the distribution such as linear, non-parametric smoothing and random effects terms will be shown. Also different modelling selection techniques and diagnostics for checking the model adequacy will be addressed. All example given are real data examples.

Bibliografia do curso:
Mikis Stasinopoulos, Rob Rigby, Gilllian Heller,. Vlasios Voudouris and Fernanda De Bastiani (2017). Flexible Regression and Smoothing: Using GAMLSS in R. Chapman and Hall/CRC

Carga Horária:

4 horas
Tipo: Obrigatória
Vagas oferecidas: 50
 
Ministrantes: Fernanda De Bastiani


 
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