Improving estimation efficiency for two-phase, outcome-dependent sampling studies

Causal inference in outcome-dependent two-phase sampling designs

We consider estimation of the causal effect of a treatment on an outcome from observational data collected in two phases. In the first phase, a simple random sample of individuals is drawn from a population. On these individuals, information is obtained on treatment, outcome and a few low dimensional covariates. These individuals are then stratified according to these factors. In the second pha...

Two Phase Sampling for Wheat Acreage Estimation

Improving Woody Biomass Estimation Efficiency Using Double Sampling

Although double sampling has been shown to be an effective method to estimate timber volume in forest inventories, only a limited body of research has tested the effectiveness of double sampling on forest biomass estimation. From forest biomass inventories collected over 9,683 ha using systematic point sampling, we examined how a double sampling scheme would have affected precision and efficien...

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...

Consistency of Semiparametric Maximum Likelihood Estimators for Two-Phase, Outcome Dependent Sampling

Semiparametric maximum likelihood estimators have recently been proposed for a class of two-phase, outcome dependent sampling models; e.g. Breslow and Holubkov (1997), Scott and Wild (1998), and Lawless, Wild, and Kalb eisch (1999). The estimators studied by these authors are predicated on the estimates of the underlying covariate distribution being concentrated on the observed covariate values...