GAMLSS for high-dimensional data – a flexible approach based on boosting

Generalized additive models for location, scale and shape (GAMLSS) are a popular semi-parametric modelling approach that, in contrast to conventional GAMs, regress not only the expected mean but every distribution parameter (e.g. location, scale and shape) to a set of covariates. Current fitting procedures for GAMLSS are infeasible for high-dimensional data setups and require variable selection based on (potentially problematic) information criteria. The present work describes a boosting algorithm for high-dimensional GAMLSS that was developed to overcome these limitations. Specifically, the new algorithm was designed to allow the simultaneous estimation of predictor effects and variable selection. The proposed algorithm was applied to data of the Munich Rental Guide, which is used by landlords and tenants as a reference for the average rent of a flat depending on its characteristics and spatial features. The net-rent predictions that resulted from the high-dimensional GAMLSS were found to be highly competitive while covariate-specific prediction intervals showed a major improvement over classical GAMs

Modelo de regressão GAMLSS para análise de sobrevivência

A análise por regressão é uma ferramenta de modelagem estatística muito utilizada no tratamento de dados. Os modelos mais tradicionais como a regressão linear simples ou os modelos lineares generalizados exigem suposições quanto ao tipo de distribuição da variável resposta e não são indicados para modelar a relações não-lineares. Para superar essas limitações, surgiram os modelos de regressão GAMLSS (Generalized additive models for location, scale and shape), que permite modelar os parâmetros de locação (μ), escala (σ) e forma (ν e τ) em função de covariáveis. Os modelos de regressão GAMLSS possibilitam o ajuste de distribuições que não pertencem à Família Exponencial e relações não-lineares entre variável resposta e covariáveis. Esses modelos de regressão podem ser extendidos para análise de sobrevivência. A presente investigação apresenta as definições dos modelos de regressão GAMLSS e uma extensão para dados de sobrevivência com fração de cura, proposta por Ramires et al (2019) [22]. O trabalho apresenta uma aplicação dessa extensão, na análise de sobrevivência de pacientes com câncer de melanoma do estado de São Paulo. A aplicação faz uma estimativa da fração de cura e demais parâmetros.Regression analysis is a statistical modeling tool widely used in data processing. More traditional models such as simple linear regression or generalized linear models require assumptions about the type of distribution of the response variable and are not suitable for modeling nonlinear relation. To overcome these limitations, the GAMLSS (Generalized additive models for location, scale and shape) regression models emerged, which allow modeling the parameters of location (μ), scale (σ) and shape (ν and τ) as a function of covariates. The GAMLSS regression models allow the fitting of distributions that do not belong to the Exponential Family and non-linear relation between the response variable and covariates. These regression models can be extended for survival analysis. The present investigation presents the definitions of the GAMLSS regression models and an extension for survival data with cured fraction, proposed by Ramires et al (2019) [22]. This work also shows an application through the survival analysis on a dataset of patients with melanoma cancer diagnosed in the state of São Paulo. In this application, an estimate of the cured fraction is made using the GAMLSS model extended to the survival analysis

A hands-on approach for fitting long-term survival models under the GAMLSS framework

In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. in this work the number of competing causes of the event of interest follows the negative binomial distribution. In this way, some well known models found in the literature are characterized as particular cases of our proposal. The model is conveniently parameterized in terms of the cured fraction, which is then linked to covariates. We explore the use of the gamlss package in R as a powerful tool for inference in long-term survival models. The procedure is illustrated with a numerical example. (C) 2009 Elsevier Ireland Ltd. All rights reserved