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 analyzed using implementations in the Bayesian software, BayesX, and some with R Codes.

Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data (Oxford Statistical Science Series)

Business & Money

Fahrmeir, Ludwig

Kneib, Thomas

Economics

Oxford University Press

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.

Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics)

Tutz, Gerhard

Education & Reference

Springer

Jeder Kredit birgt für den Kreditgeber ein Risiko, da unsicher ist, ob Kreditnehmer ihren Zahlungsverpflichtungen nachkommen. Kreditrisiken werden mit Hilfe statistischer Methoden und mathematischer Modelle gemessen. Nicht zuletzt vor dem Hintergrund Basel II hat die quantitative Kreditrisikomessung in den letzten Jahren enorm an Bedeutung gewonnen. Mit zahlreichen praxisnahen Beispielen, der ideale Einstieg für Praktiker und Quereinsteiger.

Kreditrisikomessung: Statistische Grundlagen, Methoden und Modellierung (German Edition)

Bluhm, Christian

Henking, Andreas

Finance

Statistik. Der Weg zur Datenanalyse.

Künstler, Rita

Pigeot, Iris

Springer, Berlin

Statistik. Der Weg zur Datenanalyse (Springer-Lehrbuch)

Springer, Verlag

In dem Band beschreiben die Autoren erstmals klassische Regressionsansätze und moderne nicht- und semiparametrische Methoden in einer integrierten und anwendungsorientierten Form. Um Lesern die Analyse eigener Fragestellungen zu ermöglichen, demonstrieren sie die praktische Anwendung der Konzepte und Methoden anhand ausführlicher Fallstudien. Geeignet für Studierende der Statistik sowie für Wissenschaftler und Praktiker, zum Beispiel in den Wirtschafts- und Sozialwissenschaften, der Bioinformatik und -statistik, Ökonometrie und Epidemiologie.

Regression: Modelle, Methoden und Anwendungen (Statistik und ihre Anwendungen) (German Edition)

Regression: Models, Methods and Applications

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.

Lang, Stefan

Marx, Brian

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.

Springer Nature

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.

Statistik: Der Weg zur Datenanalyse (Springer-Lehrbuch) (German Edition)

Springer Spektrum

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