Model and variable selection procedures for semiparametric time series regression
type: Item
instance of: scholarly article
title: Model and variable selection procedures for semiparametric time series regression
zbMATH Open document ID: 1201.62101
DOI: 10.1155/2009/487194
author: Risa Kato
author: Takayuki Shiohama
published in: Journal of Probability and Statistics
publication date: 2010-12-01T00:00:00Z
full work available at URL: https://eudml.org/doc/230867
review text: Summary: Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. We propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62G20
Mathematics Subject Classification ID: 65C60
Mathematics Subject Classification ID: 62F10
zbMATH DE Number: 5822135
Wikidata QID: Q58648713
MaRDI profile type: Publication
OpenAlex ID: W2171758610
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description: scientific article; zbMATH DE number 5822135