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Title: Empirical best prediction for SAE of categorical variables using finite mixtures of multinomial logistic models Authors:  Maria Giovanna Ranalli - University of Perugia (Italy) [presenting]
Maria Francesca Marino - University of Florence (Italy)
Marco Alfo - University La Sapienza, Rome (Italy)
Nicola Salvati - University of Pisa (Italy)
Abstract: Many survey variables are categorical in nature and SAE methods based on generalised linear mixed models represent a frequent tool of analysis for prediction. An Empirical Best Prediction (EBP) method is developed for responses in the Exponential Family, based on the use of area-specific, Gaussian, random effects. However, a major drawback of this approach is the computational burden required to derive estimates, compute the EBP and, in particular, provide the corresponding measure of reliability. We introduce a semiparametric EBP for categorical outcomes by extending a previous approach for univariate responses belonging to the Exponential Family of distributions. This approach leaves the mixing distribution (that is, the distribution of the area-specific random effects) unspecified and estimates it from the observed data via a NonParametric Maximum Likelihood approach. This estimate is known to be a discrete distribution defined over a finite number of locations and leads to the definition of a finite mixture specification. Finite sample properties of the proposal are tested via a simulation study. An application is also provided to data from the Italian Labour Force Survey.