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

被引:0
|
作者
Hirose, Yuichi [1 ]
机构
[1] Victoria Univ Wellington, Sch Math Stat & Operat Res, Wellington, New Zealand
关键词
efficiency; efficient information bound; efficient score; implicitly defined function; profile likelihood; semi-parametric model; MAXIMUM-LIKELIHOOD; REGRESSION-MODELS;
D O I
10.3150/14-BEJ669
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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; Statist. Plann. Inference 96 (2001) 3-27) and Murphy and van der Vaart (J. Amen 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.
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页码:589 / 614
页数:26
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