Adaptive orthogonal series estimation in additive stochastic regression models

Jiti Gao, Howell Tong, Rodney Wolff

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)

Abstract

In this paper, we consider additive stochastic nonparametric regression models. By approximating the nonparametric components by a class of orthogonal series and using a generalized cross-validation criterion, an adaptive and simultaneous estimation procedure for the nonparametric components is constructed. We illustrate the adaptive and simultaneous estimation procedure by a number of simulated and real examples.

Original languageEnglish
Pages (from-to)409-428
Number of pages20
JournalStatistica Sinica
Volume12
Issue number2
Publication statusPublished - 1 Apr 2002

Keywords

  • Adaptive estimation
  • Additive model
  • Dependent process
  • Mixing condition
  • Nonlinear time series
  • Nonparametric regression
  • Orthogonal series
  • Strict stationarity
  • Truncation parameter

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