Two-step variable selection in partially linear additive models with time series data

Mu Feng, Zhao Chen, Ximing Cheng

Research output: Contribution to journalArticle

Abstract

Lots of semi-parametric and nonparametric models are used to fit nonlinear time series data. They include partially linear time series models, nonparametric additive models, and semi-parametric single index models. In this article, we focus on fitting time series data by partially linear additive model. Combining the orthogonal series approximation and the adaptive sparse group LASSO regularization, we select the important variables between and within the groups simultaneously. Specially, we propose a two-step algorithm to obtain the grouped sparse estimators. Numerical studies show that the proposed method outperforms LASSO method in both fitting and forecasting. An empirical analysis is used to illustrate the methodology.

Original languageEnglish (US)
Pages (from-to)661-671
Number of pages11
JournalCommunications in Statistics: Simulation and Computation
Volume47
Issue number3
DOIs
StatePublished - Mar 16 2018

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Additive Models
Semiparametric Model
Nonparametric Model
Variable Selection
Time Series Data
Time series
Linear Model
Single-index Model
Orthogonal Series
Nonlinear Time Series
Empirical Analysis
Time Series Models
Forecasting
Linear Time
Numerical Study
Regularization
Estimator
Methodology
Approximation

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation

Cite this

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title = "Two-step variable selection in partially linear additive models with time series data",
abstract = "Lots of semi-parametric and nonparametric models are used to fit nonlinear time series data. They include partially linear time series models, nonparametric additive models, and semi-parametric single index models. In this article, we focus on fitting time series data by partially linear additive model. Combining the orthogonal series approximation and the adaptive sparse group LASSO regularization, we select the important variables between and within the groups simultaneously. Specially, we propose a two-step algorithm to obtain the grouped sparse estimators. Numerical studies show that the proposed method outperforms LASSO method in both fitting and forecasting. An empirical analysis is used to illustrate the methodology.",
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Two-step variable selection in partially linear additive models with time series data. / Feng, Mu; Chen, Zhao; Cheng, Ximing.

In: Communications in Statistics: Simulation and Computation, Vol. 47, No. 3, 16.03.2018, p. 661-671.

Research output: Contribution to journalArticle

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