Das Buch bietet eine umfassende Einführung in die Statistik. Die Autoren liefern eine integrierte Darstellung der deskriptiven Statistik, der modernen Methoden der explorativen Datenanalyse und der induktiven Statistik, einschließlich der Regressions- und Varianzanalyse. Zahlreiche Beispiele mit realen Daten veranschaulichen den Text. Geeignet als vorlesungsbegleitender Text, aber auch zum Selbststudium für Studierende der Wirtschafts- und Sozialwissenschaften sowie anderer Anwendungsdisziplinen und als Einführung für Studenten der Statistik.

Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric and semiparametric regression that closes the gap between theory and application. 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. It is written at an intermediate mathematical level and assumes only knowledge of basic probability, calculus, matrix algebra and statistics.

The first edition of Multivariate Statistical Modelling provided an extension of classical models for regression, time series, and longitudinal data to a much broader class including categorical data and smoothing concepts. Generalized linear modesl for univariate and multivariate analysis build the central concept, which for the modelling of complex data is widened to much more general modelling approaches. The primary aim of the new edition is to bring the book up-to-date and to reflect the major new developments over the past years. The authors give a detailed introductory survey of the subject based on the alaysis of real data drawn from a variety of subjects, including the biological sciences, economics, and the social sciences. Technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. The appendix serves as a reference or brief tutorial for the concepts of EM algorithm, numberical integration, MCMC and others. The topics covered inlude: Models for multi-categorial responses, model checking, semi- and nonparametric modelling, time series and longitudinal data, random effects models, state-space models, and survival analysis. In the new edition Bayesian concepts which are of growing importance in statistics are treated more extensively. The chapter on nonparametric and semiparametric generalized regression has been rewritten totally, random effects models now cover nonparametric maximum likelihood and fully Bayesian approaches, and state-space and hidden Markov models have been supplemented with an extension to models that can accommodate for spatial and spatiotemporal data. The authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, this book is ideally suited for applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis from econometrics, biometrics and the social sciences.

The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, 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. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.

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.

Jeder Kredit birgt fur den Kreditgeber ein Risiko, da unsicher ist, ob der Kreditnehmer seinen Zahlungsverpflichtungen nachkommen wird. Gemessen wird dieses Kreditrisiko mit Hilfe statistischer Methoden. Vor dem Hintergrund Basel II hat die Kreditrisikomessung an Bedeutung gewonnen. Dieses Buch schliesst die Lucke zwischen statistischer Grundlagenliteratur und mathematisch anspruchsvollen Werken. Es bietet einen Einstieg in die Kreditrisikomessung und die dafur notwendige Statistik. Ausgehend von den wichtigsten Begriffen zum Kreditrisiko werden deren statistische Analoga beschrieben. Enthalten sind relevante statistische Verteilungen und eine Einfuhrung in stochastische Prozesse, Portfoliomodelle und Score- bzw. Ratingmodelle. Zahlreiche praxisnahe Beispiele ermoeglichen den idealen Einstieg fur Praktiker und Quereinsteiger.

Several recent advances in smoothing and semiparametric regression are presented in this book from a unifying, Bayesian perspective. 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.

Diese Einfuhrung beschreibt erstmals klassische Regressionsansatze und moderne nicht- und semiparametrische Methoden in einer integrierten, einheitlichen und anwendungsorientierten Form. Die Darstellung wendet sich an Studierende der Statistik in Wahl- und Hauptfach sowie an empirisch-statistisch und interdisziplinar arbeitende Wissenschaftler und Praktiker, zum Beispiel in Wirtschafts- und Sozialwissenschaften, Bioinformatik, Biostatistik, OEkonometrie und Epidemiologie. Die praktische Anwendung der vorgestellten Konzepte und Methoden wird anhand ausfuhrlich vorgestellter Fallstudien demonstriert, um Lesern die Analyse eigener Fragestellungen zu ermoeglichen.

Dieses Arbeitsbuch erg nzt das Lehrbuch Statistik - Der Weg zur Datenanalyse" von Fahrmeir/K nstler/Pigeot/Tutz. Es bietet eine F lle von Aufgaben mit den dazugeh rigen, sehr ausf hrlich beschriebenen L sungen sowie Computer bungen mit realen Daten. Es dient damit der Vertiefung und selbstst ndigen Ein bung des im Lehrbuch vermittelten Stoffes zur Wahrscheinlichkeitsrechnung, deskriptiven und induktiven Statistik. Die 5. Auflage enth lt eine Reihe neuer Aufgaben, die meist in Klausuren verwendet wurden.