[Forum SIS] UNIBO Statistics Seminars

[Silvia Cagnone]

We are glad to announce the following  Statistics Seminar:





Friday, May 3, 11.30  am



Seminar Room, via delle Belle Arti 41, Bologna





Maria Giovanna Ranalli  (Università di Perugia)



Semi-Parametric Empirical Best Prediction for small area estimation of unemployment indicators





Abstract
Joint work with Maria Francesca Marino, Nicola Salvati, and Marco Alfò.
The Italian National Institute for Statistics regularly provides estimates of unemployment indicators using data from the Labor Force Survey. However, direct estimates of unemployment incidence cannot be released for Local Labor Market Areas. These are unplanned domains defined as clusters of municipalities; many are out-of-sample areas and the majority is characterized by a small sample size, which renders direct estimates inadequate. The Empirical Best Predictor represents an appropriate, model-based, alternative. However, for non-Gaussian responses, its computation and the computation of the analytic approximation to its Mean Squared Error require the solution of (possibly) multiple integrals that, generally, have not a closed form. To solve the issue, Monte Carlo methods and parametric bootstrap are common choices, even though the computational burden is a non trivial task. In this talk, we propose a Semi-Parametric Empirical Best Predictor for a (possibly) non-linear mixed effect model by leaving the distribution of the area-specific random effects unspecified and estimating it from the observed data. This approach is known to lead to a discrete mixing distribution which helps avoid unverifiable parametric assumptions and heavy integral approximations. We also derive a second-order, bias-corrected, analytic approximation to the corresponding Mean Squared Error. Finite sample properties of the proposed approach are tested via a large scale simulation study. Furthermore, the proposal is applied to unit-level data from the 2012 Italian Labor Force Survey to estimate unemployment incidence for 611 Local Labor Market Areas using auxiliary information from administrative registers and the 2011 Census.





Contact person: Daniela Cocchi





The schedule of the statistics seminars are available at http://www.stat.unibo.it/it/dipartimento/seminari-di-statistica-2019




Silvia Cagnone
Dipartimento di Scienze Statistiche "Paolo Fortunati"
Universita' di Bologna
Via delle belle arti 41 - 40126  Bologna,  ITALY
Tel: +39 051 2098213<tel:%2B39%20051%202098213>  Fax: +39 051 232153<tel:%2B39%20051%20232153>
http://www2.stat.unibo.it/cagnone/

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