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The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through numerous examples and case studies. The most important definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book’s dedicated website. Availability of (user-friendly) software has been a major criterion for the methods selected and presented.  The chapters address the classical linear model and its extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression and distributional regression models. Two appendices describe the required matrix algebra, as well as elements of probability calculus and statistical inference.   In this substantially revised and updated new edition the overview on regression models has been extended, and now includes the relation between regression models and machine learning, additional details on statistical inference in structured additive regression models have been added and a completely reworked chapter augments the presentation of quantile regression with a comprehensive introduction to distributional regression models. Regularization approaches are now more extensively discussed in most chapters of the book.   The book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. 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Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) inference, as well as empirical Bayes procedures closely related to penalized likelihood estimation and mixed models, are considered here. Throughout, the focus is on semiparametric regression and smoothing based on basis expansions of unknown functions and effects in combination with smoothness priors for the basis coefficients.  Beginning with a review of basic methods for smoothing and mixed models, longitudinal data, spatial data and event history data are treated in separate chapters. Worked examples from various fields such as forestry, development economics, medicine and marketing are used to illustrate the statistical methods covered in this book. Most of these examples have been analysed using implementations in the Bayesian software, BayesX, and some with R Codes. These, as well as some of the data sets, are made publicly available on the website accompanying this book.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52323515105553,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":52323515728145,"sku":"NGR9780199533022","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9780199533022.jpg?v=1776940647"},{"product_id":"multivariate-statistische-verfahren-book-ludwig-fahrmeir-9783110138061","title":"Multivariate statistische Verfahren","description":"Keine ausführliche Beschreibung für \"Multivariate statistische Verfahren\" verfügbar.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52327455818001,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52327456309521,"sku":"NLS9783110138061","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783110138061.jpg?v=1758062537"},{"product_id":"advances-in-glim-and-statistical-modelling-book-ludwig-fahrmeir-9780387978734","title":"Advances in GLIM and Statistical Modelling","description":"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. 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