Time: November 23rd, 1 pm - 2 pm
Place: B705

Population surveys are carried out via sampling designs and data collection of individual units with the intention of making statistical inferences about a larger population of which these units are members. These surveys are usually designed to provide efficient estimates of parameters of interest for large populations. In most cases, surveys are not originally designed to produce estimates for small domains and hence these domains are poorly represented in the sample. Thus, the surveys often provide very little information on a small area level and direct survey estimates on a target small area are not reliable due to a small sample size connected to this area. With the intention of solving small area estimation problems, we have proposed a multivariate linear model for repeated measures data. The aim is to obtain a model which borrows strength both across small areas and over time. The model accounts for repeated surveys, grouped response units and random effects variations. Estimation of model parameters is discussed within a likelihood based approach. Prediction of random effects, small area means across time points and per group units are derived. A parametric bootstrap method is proposed for estimating the mean squared error of the predicted small area means. Results are supported by a simulation study.