Bernoulli

  • Bernoulli
  • Volume 22, Number 1 (2016), 589-614.

On differentiability of implicitly defined function in semi-parametric profile likelihood estimation

Yuichi Hirose

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Abstract

In this paper, we study the differentiability of implicitly defined functions which we encounter in the profile likelihood estimation of parameters in semi-parametric models. Scott and Wild (Biometrika 84 (1997) 57–71; J. Statist. Plann. Inference 96 (2001) 3–27) and Murphy and van der Vaart (J. Amer. Statist. Assoc. 95 (2000) 449–485) developed methodologies that can avoid dealing with such implicitly defined functions by parametrizing parameters in the profile likelihood and using an approximate least favorable submodel in semi-parametric models. Our result shows applicability of an alternative approach presented in Hirose (Ann. Inst. Statist. Math. 63 (2011) 1247–1275) which uses the direct expansion of the profile likelihood.

Article information

Source
Bernoulli, Volume 22, Number 1 (2016), 589-614.

Dates
Received: August 2013
Revised: April 2014
First available in Project Euclid: 30 September 2015

Permanent link to this document
https://projecteuclid.org/euclid.bj/1443620861

Digital Object Identifier
doi:10.3150/14-BEJ669

Mathematical Reviews number (MathSciNet)
MR3449794

Zentralblatt MATH identifier
06543281

Keywords
efficiency efficient information bound efficient score implicitly defined function profile likelihood semi-parametric model

Citation

Hirose, Yuichi. On differentiability of implicitly defined function in semi-parametric profile likelihood estimation. Bernoulli 22 (2016), no. 1, 589--614. doi:10.3150/14-BEJ669. https://projecteuclid.org/euclid.bj/1443620861


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