Nonparametric Inference for Copulas and Measures of Dependence Under Length-Biased Sampling and Informative Censoring

Nonparametric Inference Under Biased Sampling from a Finite Population

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...

Estimation in a competing risks proportional hazards model under length-biased sampling with censoring.

What population does the sample represent? The answer to this question is of crucial importance when estimating a survivor function in duration studies. As is well-known, in a stationary population, survival data obtained from a cross-sectional sample taken from the population at time t(0) represents not the target density f (t) but its length-biased version proportional to t f (t), for t > 0. ...

Scalable Approximate Bayesian Inference for Outlier Detection under Informative Sampling

Government surveys of business establishments receive a large volume of submissions where a small subset contain errors. Analysts need a fast-computing algorithm to flag this subset due to a short time window between collection and reporting. We offer a computationallyscalable optimization method based on non-parametric mixtures of hierarchical Dirichlet processes that allows discovery of multi...

Inference for the location and scale parameters of the Rayleigh distribution under type-II double censoring

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