Statistics Seminar: Ray Chambers -- M-Quantile Regression for Binary Data wih Application to Small Area Estimation

Ray Chambers Centre for Statistical and Survey Methodology (CSSM) University of
Wollongong 

Location: Carslaw 273 

Time: 2pm Friday, May 11, 2012 

Title: M-Quantile Regression for Binary Data wih Application to Small Area Estimation 

Abstract: M-quantile regression models were first proposed in Breckling and Chambers
(1988), and were first applied to small area estimation by Chambers and Tzavidis
(2006).  These models represent a robust and flexible alternative to the widespread use
of random effects models in small area estimation.  However, since quantiles, and more
generally M-quantiles, are only uniquely defined for continuous variables, M-quantile
models have to date only been applicable when the variable of interest is continuously
distributed.  In this presentation I will show how the M-quantile regression approach
can be extended to binary data, and more generally to categorical data.  I will then
apply this approach to estimation of the small area average of a binary variable (i.e.
a proportion).  The current industry standard for estimating such a proportion is to use
a plug-in version of the Empirical Best predictor based on a mixed model for the logit
of the probability that the target binary variable takes the value one.  I will show
results from both model-based and design-based simulations that compare the binary
M-quantile predictor and the plug-in EB predictor.  Some tentative conclusions about the
usefulness of the binary M-quantile approach will be made.  

Joint work with Nicola Salvati (DSMAE, University of Pisa) and Nikos Tzavidis (S3RI,
University of Southampton)