Tests for stochastic ordering under biased sampling

Depth-Scaled Biased Stochastic Sampling

Several techniques have recently been developed that are based on discrepancy analysis in heuristic-based search trees. We offer a comparison of two of these proposed techniques: depth-bounded discrepancy search (DDS) and heuristic-biased stochastic sampling (HBSS), and one new technique, depth-scaled biased stochastic sampling (DSBSS). The new technique uses the framework of HBSS, but takes in...

Empirical likelihood-based tests for stochastic ordering.

This paper develops an empirical likelihood approach to testing for the presence of stochastic ordering among univariate distributions based on independent random samples from each distribution. The proposed test statistic is formed by integrating a localized empirical likelihood statistic with respect to the empirical distribution of the pooled sample. The asymptotic null distribution of this ...

Semi-parametric Estimates under Biased Sampling

In observational studies subjects may self select, thereby creating a biased sample. Such problems arise frequently, for example, in astronomical, biomedical, animal, and oil studies, survey sampling and econometrics. For a typical subject, let Y denote the value of interest and suppose that Y has an unknown density function f . Further, let w(y) denote the probability that the subject includes...

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.

Nonparametric Inference Under Biased Sampling from a Finite Population