Weighted likelihood estimation under two-phase sampling

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...

Material 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 ,...

Optimal Calibration Estimators under Two-phase Sampling

Two Phase Sampling for Wheat Acreage Estimation

Asymptotic Normality under Two-Phase Sampling Designs

Large sample properties of statistical inferences in the context of finite populations are harder to determine than in the iid case due to their dependence jointly on the characteristics of the finite population and the sampling design employed. There have been many discussions on special inference procedures under special sampling designs in the literature. General and comprehensive results ar...