Generalized linear models with clustered data

Sammanfattning: In situations where a large data set is partitioned into many relatively small groups, and where the members within a group have some common unmeasured characteristics, the number of parameters requiring estimation tends to increase with sample size if a fixed effects model is applied. This fact causes the assumptions underlying asymptotic results to be violated.The first paper in this thesis considers two possible solutions to this problem, a random intercepts model and a fixed effects model, where asymptotics are replaced by a simple form of bootstrapping. A profiling approach is introduced in the fixed effects case, which makes it computationally efficient even with a huge number of groups. The grouping effect is mainly seen as a nuisance in this paper.In the second paper the effect of misspecifying the distribution of the random effects in a generalized linear mixed model for binary data is studied. One problem with mixed effects models is that the distributional assumptions about the random effects are not easily checked from real data. Models with Gaussian, logistic and Cauchy distributional assumptions are used for parameter estimation on data simulated using the same three distributions. The eect of these assumptions on parameter estimation is presented. Two criteria for model selection are investigated, the Akaike information criterion and a criterion based on a X2 statistic. The estimators for fixed effects parameters are quite robust against misspecification of the random effects distribution, at least with the distributions used in this paper. Even when the true random effects distribution is Cauchy, models assuming a Gaussian or a logistic distribution regularly produce estimates with less bias.In the third paper the results from the first two papers are applied to infant mortality data. We found that there was significant clustering of infant mortality in the Skellefteå region in the years 1831-1890. An "ad hoc" method for comparing the magnitude of unexplained clustering after a model is applied is also presented.The last paper of this thesis is concerned with the problem of testing for spatial clustering caused by autocorrelation. A test that is robust against heteroscedasticity is proposed. In a simulation study the properties of the proposed statistic, K, are investigated. The power of the test based on K is compared to that of Moran's I in the simulation study. Both tests are then applied to mortality data from Swedish municipalities.