Title	Advances in GLIM and Statistical Modelling [electronic resource] : Proceedings of the GLIM92 Conference and the 7th International Workshop on Statistical Modelling, Munich, 13-17 July 1992 / edited by Ludwig Fahrmeir, Brian Francis, Robert Gilchrist, Gerhard Tutz
Imprint	New York, NY : Springer New York, 1992
Connect to	http://dx.doi.org/10.1007/978-1-4612-2952-0
Descript	X, 225 p. online resource

This volume presents the published Proceedings of the joint meeting of GUM92 and the 7th International Workshop on Statistical Modelling, held in Munich, Germany from 13 to 17 July 1992. The meeting aimed to bring together researchers interested in the development and applications of generalized linear modelling in GUM and those interested in statistical modelling in its widest sense. This joint meeting built upon the success of previous workshops and GUM conferences. Previous GUM conferences were held in London and Lancaster, and a joint GUM Conference/4th Modelling Workshop was held in Trento. (The Proceedings of previous GUM conferences/Statistical Modelling Workshops are available as numbers 14 , 32 and 57 of the Springer Verlag series of Lecture Notes in Statistics). Workshops have been organized in Innsbruck, Perugia, Vienna, Toulouse and Utrecht. (Proceedings of the Toulouse Workshop appear as numbers 3 and 4 of volume 13 of the journal Computational Statistics and Data Analysis). Much statistical modelling is carried out using GUM, as is apparent from many of the papers in these Proceedings. Thus the Programme Committee were also keen on encouraging papers which addressed problems which are not only of practical importance but which are also relevant to GUM or other software development. The Programme Committee requested both theoretical and applied papers. Thus there are papers in a wide range of practical areas, such as ecology, breast cancer remission and diabetes mortality, banking and insurance, quality control, social mobility, organizational behaviour

Papers Presented in the GLIM Working Party Session -- The New Facilities of GLIM4 -- Model Fitting Applications in GLIM4 -- Medical Applications in GLIM4 -- Invited and Contributed Papers -- Residuals and Influential Points in Mean Structures Estimated with Pseudo Maximum Likelihood Methods -- Fitting the Continuation Ratio Model using GLIM4 -- A New Approach for the Analysis of Multi-Episode Duration Data -- Approaches to Estimation with Errors in Predictors -- The Frontit Model: A Stochastic Frontier for Dichotomic Random Variables -- Investigation of an Ordered Logistic Model for Consumer Debt -- On Link Selection in Generalized Linear Models -- Estimation of Heterogeneity โ{128}{148} A GLM-Approach -- Generalized Linear Models with P-splines -- Modelling Excess Mortality of Diabetics: Generalised Linear Models and Dynamic Estimation -- Smoothing in Dynamic Generalized Linear Models by Gibbs Sampling -- Bias Reduction, the Jeffreys Prior and GLIM -- Approximate Predictive Integrals for Dynamic Generalized Linear Models -- Symmetric Interaction Models to Study Innovation Processes in the Software Industry -- Ordinal Time Series Models with Application to Forest Damage Data -- Choosing Between Non-nested Models: A Simulation Approach -- A Class of Models for the Simultaneous Analysis of Square Contingency Tables -- Estimation of the Parameters of the Bilinear Association Model using the Gibbs Sampler -- Marginal Modelling of Correlated Ordinal Data using an n-way Plackett Distribution -- The Evaluation of Bank Accounts using Optimized Moving Local Regression -- Common Factor Model: Stochastic Model, Data Analysis Technique Or What? -- The Design of Experiments to Discriminate Between Two Rival Generalized Linear Models -- A Changepoint Model for Infant Survival in the British Births Survey -- The Construction of Life and Other Actuarial Graduated Tables using GLIM -- Age-Period-Cohort Models: A Comparison of Methods -- Estimation of a First Order Autoregressive Process with Poisson Marginals for Count Data -- Saddlepoint Approximations for Generalized Linear Models: A Gentle Introduction -- Robust Poisson Regression -- Parametric and Seminonparametric Analysis of Nonlinear Time Series -- Nonparametric Approaches to Generalized Linear Models