Nonparametric Inference Under Biased Sampling from a Finite Population

Nonparametric Inference Under Biased Sampling from a Finite Population

Bayesian Nonparametric Weighted Sampling Inference

It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the presence of inverse-probability weights. We use a hierarchical approach in which we model the distribution of the weights of the nonsampled units in the population and simultaneously include them as predictors in a nonpara...

Nonparametric Regression Estimation of Finite Population Totals under Two-Stage Sampling

We consider nonparametric regression estimation for finite population totals for two-stage sampling, in which complete auxiliary information is available for first-stage sampling units. The estimators, based on local polynomial regression, are linear combinations of cluster total estimators, with weights that are calibrated to known control totals. The estimators are asymptotically design-unbia...

Nonparametric estimation of a distribution function under biased sampling and censoring

Abstract: This paper derives the nonparametric maximum likelihood estimator (NPMLE) of a distribution function from observations which are subject to both bias and censoring. The NPMLE is obtained by a simple EM algorithm which is an extension of the algorithm suggested by Vardi (Biometrika, 1989) for size biased data. Application of the algorithm to many models is discussed and a simulation st...

Estimating a treatment effect under biased sampling.

Methods are presented for the estimation of a treatment effect based on before- and after-treatment values, where for ethical reasons all and only those patients are treated whose before-treatment values exceed a given constant.