Date: 2013-10-04

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Mixed models are commonly used for the analysis data in small area estimation. In particular, small area estimation has been extensively studied under linear mixed models. However, in practice there are many situations that we have counts or proportions in small area estimation; for example a (monthly) dataset on the number of incidences in small areas. Recently, small area estimation under the linear mixed model with penalized spline model, for xed part of the model, was studied. In this talk, small area estimation under generalized linear mixed models by combining time series and cross-sectional data with the extension of these models to include penalized spline regression models are proposed. A likelihood-based approach is used to predict small area parameters and also to provide prediction intervals. The performance of the proposed models and approach is evaluated through simulation studies and also by real datasets.

Speaker

Farhad Shokoohi is a Postdoctoral Fellow in our department. He works with Masoud Asgharian and Abbas Khalili.