Statistical Modelling

NPML estimation of the mixing distribution in general statistical models with unobserved random effects.- Some topics in optimum experimental design for generalized linear models.- Autoregressive modelling of Markov chains.- A case-study on accuracy of cytological diagnosis.- Dynamics and correlated responses in longitudinal data models.- Bootstrap methods for generalized linear mixed models with applications to small area estimation.- Confidence intervals for threshold parameters.- Optimal design for models incorporating the Richards function.- Mixed Markov renewal models of social processes.- Statistical inference based on a general model of unobserved heterogeneity.- An extended model for paired comparisons.- Indirect observations, composite link models and penalized likelihood PAUL.- Model estimation in nonlinear regression.- Pearson statistics, goodness of fit, and overdispersion in generalised linear models.- M-estimation: Some remedies.- Subject-specific and population-averaged questions for log-linear regression data.- Radon and lung cancer mortality: An example of a Bayesian ecological analysis.- IRREML, a tool for fitting a versatile class of mixed models for ordinal data.- Towards a general robust estimation approach for generalised regression models.- Comparing local fitting to other automatic smoothers.- Iterative reweighted partial least squares estimation for GLMs.- Protective estimation of longitudinal categorical data with nonrandom dropout.- Analysis of counts generated by an age-dependent branching process.- Quantitative risk assessment for clustered binary data.- Optimal design and lack fit in nonlinear regression models.- Nonparametric regression, kriging and process optimization.- Frailty in multiplicative intensity models.- Methods for assessing the adequacy of probability prediction models.- Forecast methods in regression models for categorical time series.- Standard errors, correlations and model analysis.- Mean and dispersion additive

Gilg Seeber ist Professor für Statistik am Institut für Politikwissenschaft der Universität Innsbruck. Reinhold Hatzinger ist Professor an der Wirtschaftsuniversität Wien und leitet den Bereich quantitative empirische Methoden