Efficient statistical inference procedures for partially nonlinear models and their applications

Runze Li, Lei Nie

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Motivated by an analysis of a real data set in ecology, we consider a class of partially nonlinear models where both a nonparametric component and a parametric component are present. We develop two new estimation procedures to estimate the parameters in the parametric component. Consistency and asymptotic normality of the resulting estimators are established. We further propose an estimation procedure and a generalized F-test procedure for the nonparametric component in the partially nonlinear models. Asymptotic properties of the newly proposed estimation procedure and the test statistic are derived. Finite sample performance of the proposed inference procedures are assessed by Monte Carlo simulation studies. An application in ecology is used to illustrate the proposed methods.

Original languageEnglish (US)
Pages (from-to)904-911
Number of pages8
JournalBiometrics
Volume64
Issue number3
DOIs
StatePublished - Sep 2008

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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