Objective stepwise Bayes weights in survey sampling

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Jeremy Strief and Glen Meeden1

Abstract

Although weights are widely used in survey sampling their ultimate justification from the design perspective is often problematical. Here we will argue for a stepwise Bayes justification for weights that does not depend explicitly on the sampling design. This approach will make use of the standard kind of information present in auxiliary variables however it will not assume a model relating the auxiliary variables to the characteristic of interest. The resulting weight for a unit in the sample can be given the usual interpretation as the number of units in the population which it represents.

Key Words

Sample survey; Weights; Bayesian inference.

Table of content

1 Introduction

2 The Polya posterior

3 The constrained Polya posterior

4 Constrained Polya posterior weights

5 The weighted Dirichlet posterior

6 Weights and Horvitz-Thompson

7 Examples

8 Final remarks

 

 

 

 

 


1Jeremy Strief, Principal Statistician, Medtronic Energy and Component Center, Brooklyn Center, MN 55430. E-mail : jstrief@gmail.com; Glen Meeden, School of Statistics, University of Minnesota, Minneapolis, MN 55455. E-mail : glen@stat.umn.edu.

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