Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK. (18th May 2015)

• Record Type: Journal Article
• Authors: Chambers, Ray; Salvati, Nicola; Tzavidis, Nikos
• Is Part Of: Journal of the Royal Statistical Society. Volume 179:Number 2(2016:Apr.)
• Page Start: 453
• Page End: 479
• Publication Date: 2015-05-18
• Subjects: Generalized linear mixed model -- M-estimation -- M-quantiles -- Robust inference -- UK Labour Force Survey; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12123
• Languages: English
• ISSNs: 0964-1998

Abstract

Summary: A new semiparametric and robust approach to small area estimation for discrete outcomes is proposed. The methodology represents an efficient and easily computed alternative to prediction by using a generalized linear mixed model and is based on an extension of M -quantile regression. In addition, two estimators of the prediction mean-squared error are described: one based on Taylor linearization and another based on the block bootstrap. The methodology proposed is applied to UK annual Labour Force Survey data for estimating the proportion of the unemployed in local authorities in the UK. The properties of estimators are further empirically assessed in model-based simulations.