Publication
Title
A flexible semiparametric regression model for bimodal, asymmetric and censored data
Author
Abstract
In this paper, we propose a new semiparametric heteroscedastic regression model allowing for positive and negative skewness and bimodal shapes using the B-spline basis for nonlinear effects. The proposed distribution is based on the generalized additive models for location, scale and shape framework in order to model any or all parameters of the distribution using parametric linear and/or nonparametric smooth functions of explanatory variables. We motivate the new model by means of Monte Carlo simulations, thus ignoring the skewness and bimodality of the random errors in semiparametric regression models, which may introduce biases on the parameter estimates and/or on the estimation of the associated variability measures. An iterative estimation process and some diagnostic methods are investigated. Applications to two real data sets are presented and the method is compared to the usual regression methods.
Language
English
Source (journal)
Journal of applied statistics. - Abingdon
Publication
Abingdon : 2018
ISSN
0266-4763
Volume/pages
45 :7 (2018) , p. 1303-1324
ISI
000429230000010
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 04.05.2018
Last edited 20.09.2021
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