The semiparametric regression model for bimodal data with different penalized smoothers applied to climatology, ethanol and air quality data.

Autor: Vasconcelos JCS; ESALQ, Universidade de S ao Paulo, Piracicaba, Brazil., Cordeiro GM; UFPE, Universidade Federal de Pernambuco, Recife, Brazil., Ortega EMM; ESALQ, Universidade de S ao Paulo, Piracicaba, Brazil.
Jazyk: angličtina
Zdroj: Journal of applied statistics [J Appl Stat] 2020 Aug 07; Vol. 49 (1), pp. 248-267. Date of Electronic Publication: 2020 Aug 07 (Print Publication: 2022).
DOI: 10.1080/02664763.2020.1803812
Abstrakt: Semiparametric regressions can be used to model data when covariables and the response variable have a nonlinear relationship. In this work, we propose three flexible regression models for bimodal data called the additive, additive partial and semiparametric regressions, basing on the odd log-logistic generalized inverse Gaussian distribution under three types of penalized smoothers, where the main idea is not to confront the three forms of smoothings but to show the versatility of the distribution with three types of penalized smoothers. We present several Monte Carlo simulations carried out for different configurations of the parameters and some sample sizes to verify the precision of the penalized maximum-likelihood estimators. The usefulness of the proposed regressions is proved empirically through three applications to climatology, ethanol and air quality data.
Competing Interests: No potential conflict of interest was reported by the authors.
(© 2020 Informa UK Limited, trading as Taylor & Francis Group.)
Databáze: MEDLINE
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