On single-index coefficient regression models

Yingcun XIA, Wai Keung LI

Research output: Contribution to journalArticles

109 Citations (Scopus)

Abstract

In this article we investigate a class of single-index coefficient regression models under dependence. This includes many existing models, such as the smooth transition threshold autoregressive (STAR) model of Chan and Tong, the functional-coefficient autoregressive (FAR) model of Chen and Tsay, and the single-index model of Ichimura. Compared to the varying-coefficient model of Hastie and Tibshirani, our model can avoid the curse of dimensionality in multivariate nonparametric estimations. Another advantage of this model is that a threshold variable is chosen automatically. An estimation method is proposed, and the corresponding estimators are shown to be consistent and asymptotically normal. Some simulations and applications are also reported. Copyright © 1999 American Statistical Association.
Original languageEnglish
Pages (from-to)1275-1285
JournalJournal of the American Statistical Association
Volume94
Issue number448
DOIs
Publication statusPublished - 1999

Citation

Xia, Y., & Li, W. K. (1999). On single-index coefficient regression models. Journal of the American Statistical Association, 94(448), 1275-1285. doi: 10.1080/01621459.1999.10473880

Keywords

  • Kernel smoothing
  • Nonparametric time series
  • Single-index model
  • Strongly mixing
  • Varying-coefficient model

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