Statistical inference in single-index and partially nonlinear models

Jiti Gao, Hua Liang

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20 Citations (Scopus)

Abstract

A finite series approximation technique is introduced. We first apply this approximation technique to a semiparametric single-index model to construct a nonlinear least squares (LS) estimator for an unknown parameter and then discuss the confidence region for this parameter based on the asymptotic distribution of the nonlinear LS estimator. Meanwhile, a computational algorithm and a small sample study for this nonlinear LS estimator are developed. Additionally, we apply the finite series approximation technique to a partially nonlinear model and obtain some new results.

Original languageEnglish
Pages (from-to)493-517
Number of pages25
JournalAnnals of the Institute of Statistical Mathematics
Volume49
Issue number3
DOIs
Publication statusPublished - 1 Jan 1997

Keywords

  • Asymptotic normality
  • Finite series approximation
  • Partially nonlinear model
  • Semiparametric single-index regression model

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