Estimation of a groupwise additive multiple-index model and its applications

Tao Wang, Jun Zhang, Hua Liang, Lixing ZHU

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

In this paper, we propose a simple linear least squares framework to deal with estimation and selection for a groupwise additive multiple-index model, of which the partially linear single-index model is a special case, and in which each component function has a single-index structure. We show that, somewhat unexpectedly, all index vectors can be recovered through a single least squares coefficient vector. As a direct application, for partially linear single-index models we develop a new two-stage estimation procedure that is iterative-free and easily implemented. This estimation approach can also be applied to develop, for the semi-parametric model under study, a penalized least squares estimation and establish its asymptotic behavior in sparse and high-dimensional settings without any nonparametric treatment. A simulation study and a data analysis are presented.

Original languageEnglish
Pages (from-to)551-566
Number of pages16
JournalStatistica Sinica
Volume25
Issue number2
DOIs
Publication statusPublished - Apr 2015

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • High dimensionality
  • Index estimation
  • Least squares
  • Multipleindex models
  • Variable selection

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