Model and Variable Selection Procedures for Semiparametric Time Series Regression
Joint Authors
Source
Journal of Probability and Statistics
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-37, 37 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-09-22
Country of Publication
Egypt
No. of Pages
37
Main Subjects
Abstract EN
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.
In this paper, 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.
American Psychological Association (APA)
Kato, Risa& Shiohama, Takayuki. 2009. Model and Variable Selection Procedures for Semiparametric Time Series Regression. Journal of Probability and Statistics،Vol. 2009, no. 2009, pp.1-37.
https://search.emarefa.net/detail/BIM-475558
Modern Language Association (MLA)
Kato, Risa& Shiohama, Takayuki. Model and Variable Selection Procedures for Semiparametric Time Series Regression. Journal of Probability and Statistics No. 2009 (2009), pp.1-37.
https://search.emarefa.net/detail/BIM-475558
American Medical Association (AMA)
Kato, Risa& Shiohama, Takayuki. Model and Variable Selection Procedures for Semiparametric Time Series Regression. Journal of Probability and Statistics. 2009. Vol. 2009, no. 2009, pp.1-37.
https://search.emarefa.net/detail/BIM-475558
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-475558