Two-phase sampling experiment for propensity score estimation in self-selected samples

Jackknife Variance Estimation for Two Samples after Imputation under Two-Phase Sampling

We propose a jackknife variance estimator for the population average from two, two-phase samples after imputation. The jackknife method has long been used to estimate and reduce bias, but has now become a valuable tool for variance estimation. We apply two different sampling methods, (simple random sampling and stratified random sampling) to derive jackknife variance estimators for the twosampl...

Two Phase Sampling for Wheat Acreage Estimation

Model feedback in Bayesian propensity score estimation.

Methods based on the propensity score comprise one set of valuable tools for comparative effectiveness research and for estimating causal effects more generally. These methods typically consist of two distinct stages: (1) a propensity score stage where a model is fit to predict the propensity to receive treatment (the propensity score), and (2) an outcome stage where responses are compared in t...

Jackknife Variance Estimation for Two-phase Samples with High Sampling Fractions

The two-phase sample design is often employed in sample surveys for various reasons. It has a long history, first introduced by Neyman (1938). Traditionally, the technique is used to collect some auxiliary data that are not available for the sampling frame from a large first-phase sample to use the data at the second-phase sampling. The technique, sometimes called “double sampling” has many app...

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...