Variable selection in partially time-varying coefficient models

Research output: Contribution to journalArticle



Publication details

JournalJournal of Nonparametric Statistics
DateE-pub ahead of print - 21 Aug 2009
DatePublished (current) - 21 Aug 2009
Issue number5
Number of pages14
Pages (from-to)553-566
Early online date21/08/09
Original languageEnglish


A partially time-varying coefficient model is introduced to characterise the nonlinearity and trending phenomenon. To enhance predictability and to select significant variables in the parametric component of the model, the penalised least squares method with the help of the profile local linear technique is developed in this article. The convergence rate and the oracle property of the resulting estimator are established under mild conditions. A remarkable achievement of our results is that it does not require undersmoothing of the nonparametric component. Meanwhile, some extensions of the proposed model and method are also discussed. Furthermore, some numerical examples are provided to show that our theory and method work well in practice.

    Research areas

  • penalised least squares, oracle property, SERIES, time-varying coefficient model, semiparametric regression, LINEAR-MODEL, NONCONCAVE PENALIZED LIKELIHOOD, SHRINKAGE, profile local linear technique

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