Penalized Balanced Sampling
F. Jay Breidt, Department of Statistics, Colorado State University
Monday, February 8, 2010
4:00 p.m., Weber 223
Linear mixed models are flexible and extensible models
that cover a wide range of statistical methods. They have found many
uses in estimation for complex surveys, particularly in small area
estimation and in extensions of generalized regression estimation.
They have also been used as a means of relaxing constraints in
calibration estimation. The purpose of this work is to consider
methods by which linear mixed models may be used at the design stage
of a survey. This paper reviews the ideas of balanced sampling and
the cube algorithm, and proposes an implementation of the cube
algorithm by which penalized balanced samples can be selected. Such
samples have the property that Horvitz-Thompson estimators from
penalized balanced samples behave like linear mixed model-assisted
survey regression estimators from unbalanced samples. The
methodology is evaluated by using nonparametric and temporal linear
mixed models in simulation experiments, and by using a spatial
linear mixed model in an artificial but realistic sampling
application, motivated by a 1991--1996 Environmental Protection Agency
survey of lakes in the Northeastern states of the United States.
This is joint work with Guillaume Chauvet, Ecole Nationale de la
Statistique et de l'Analyse de l'Information.