Estimation and hypothesis test for partial linear single-index multiplicative models

Jun Zhang*, Xia Cui, Heng PENG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Estimation and hypothesis test for partial linear single-index multiplicative models are considered in this paper. To estimate unknown single-index parameter, we propose a profile least product relative error estimator coupled with a leave-one-component-out method. To test a hypothesis on the parametric components, a Wald-type test statistic is proposed. We employ the smoothly clipped absolute deviation penalty to select relevant variables. To study model checking problem, we propose a variant of the integrated conditional moment test statistic by using linear projection weighting function, and we also suggest a bootstrap procedure for calculating critical values. Simulation studies are conducted to demonstrate the performance of the proposed procedure and a real example is analyzed for illustration.

Original languageEnglish
Pages (from-to)699-740
Number of pages42
JournalAnnals of the Institute of Statistical Mathematics
Volume72
Issue number3
DOIs
Publication statusPublished - 1 Jun 2020

Scopus Subject Areas

  • Statistics and Probability

User-Defined Keywords

  • Local linear smoothing
  • Model checking
  • Profile least product relative error estimator
  • Single-index
  • Variable selection

Fingerprint

Dive into the research topics of 'Estimation and hypothesis test for partial linear single-index multiplicative models'. Together they form a unique fingerprint.

Cite this