Student Seminar Series - January 27, 2005
University of Minnesota
School of Statistics
College of Liberal Arts

Computations for Bayesian Procedures under Linear Constraints in Finite Population Sampling

Radu C. Lazar



Thursday, January 27, 2005
3:30 PM, 115 Ford Hall
Minneapolis, East Bank Campus


Refreshments at 3:00 PM
300 Ford Hall



Abstract

 In survey sampling, one is interested in estimating population quantities such as the mean or median. Prior information is often available through the presence of auxiliary variables. For example, the population mean or median of an auxiliary variable may either be known exactly or known to lie in some interval. We will present a Bayesian estimation method which takes into account such prior information which in some cases cannot be used by the standard estimation procedures. Various situations are considered wherein the prior information induces linear constraints on the underlying space of the posterior distribution, once the sample has been observed. It is shown how one can estimate the population mean by sampling from such posterior distributions via a Markov Chain Monte Carlo sampler over such restricted spaces. The Bayesian estimation method makes use of the prior information effectively and yields estimates with good frequentist properties.