Gerhard Tutz Reihenfolge der Bücher

This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e. g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book. 

Focusing on recent advancements in flexible and high-dimensional regression, this book explores topics often overlooked in traditional categorical data analysis literature. It delves into innovative methodologies and techniques, providing a comprehensive resource for researchers and practitioners interested in the evolving landscape of statistical analysis.

InhaltsverzeichnisFrontmatter -- Vorwort zur zweiten Auflage -- Vorwort zur ersten Auflage -- Inhalt -- Kapitel 1. Einführung -- Kapitel 2. Mehrdimensionale Zufallsvariablen und Verteilungen -- Kapitel 3. Grundlegende multivariate Schätz- und Testprobleme -- Kapitel 4. Regressionsanalyse -- Kapitel 5. Varianz- und Kovarianzanalyse -- Kapitel 6. Kategoriale und generalisierte lineare Regression -- Kapitel 7. Regressionsmodelle zur Analyse von Verweildauern -- Kapitel 8. Diskriminanzanalyse -- Kapitel 9. Clusteranalyse -- Kapitel 10. Zusammenhangsanalysen in mehrdimensionalen Kontingenztabellen - das loglineare Modell -- Kapitel 11. Modelle mit latenten Variablen: Faktorenanalyse, Latent-Structure- Analyse und LISREL-Analyse -- Kapitel 12. Grundlagen der mehrdimensionalen Skalierung -- Anhang A. Grundbegriffe der Matrix-Algebra -- Anhang B. Tabellen -- Literatur -- Wichtige Programmpakete und Programmierumgebungen -- Register -- Backmatter

Das Werk gibt eine Einführung in die Kategoriale Regression, die auch für Anwender sehr gut geeignet ist. Neben wirtschaftswissenschaftlichen Beispielen, die zahlenmäßig dominieren, finden sich Beispiele aus Biometrie, der medizinischen Statistik, Psychologie und Demographie.