Type: Seminar

Distribution: World

Expiry: 11 May 2012

Auth: uri@purix (assumed)

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)