Calibration weighted estimation of semiparametric transformation models for two-phase sampling
نویسندگان
چکیده
منابع مشابه
Weighted Likelihood Estimation under Two-phase Sampling.
We develop asymptotic theory for weighted likelihood estimators (WLE) under two-phase stratified sampling without replacement. We also consider several variants of WLEs involving estimated weights and calibration. A set of empirical process tools are developed including a Glivenko-Cantelli theorem, a theorem for rates of convergence of M-estimators, and a Donsker theorem for the inverse probabi...
متن کاملMaterial for “ Weighted Likelihood Estimation under Two - Phase Sampling
A. Appendix. We repeatedly use the notation for empirical measures and processes introduced in Section 2 following [2]. The fundamental idea of [2] is to view Gξj,Nj as the exchangeably weighted bootstrap empirical process corresponding to Gj,Nj ≡ √ Nj ( Pj,Nj − P0|j ) for j = 1, . . . , J . The processes Gξj,Nj converge weakly to √ pj(1− pj)Gj for independent P0|jBrownian bridge processes Gj ,...
متن کاملEfficient estimation of semiparametric transformation models for counting processes
A class of semiparametric transformation models is proposed to characterise the effects of possibly time-varying covariates on the intensity functions of counting processes. The class includes the proportional intensity model and linear transformation models as special cases. Nonparametric maximum likelihood estimators are developed for the regression parameters and cumulative intensity functio...
متن کاملConsistency of Semiparametric Maximum Likelihood Estimators for Two-Phase Sampling
Semiparametric maximum likelihood estimators have recently been proposed for a class of two-phase, outcome-dependent sampling models. All of them were "restricted" maximum likelihood estimators, in the sense that the maximization is carried out only over distributions concentrated on the observed values of the covariate vectors. In this paper, the authors give conditions for consistency of thes...
متن کاملHeteroscedastic semiparametric transformation models: estimation and testing for validity
In this paper we consider a heteroscedastic transformation model of the form Λθ(Y ) = m(X) + σ(X)ε, where Λθ belongs to a parametric family of monotone transformations, m(·) and σ(·) are unknown but smooth functions, ε is independent of the d-dimensional vector of covariates X, E(ε) = 0 and Var(ε) = 0. In this model, we first consider the estimation of the unknown components of the model, namel...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics in Medicine
سال: 2015
ISSN: 0277-6715
DOI: 10.1002/sim.6439