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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.
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.
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.
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.
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.
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.
Das Buch bietet eine umfassende Einfuhrung 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 fur Studierende der Wirtschafts-
und Sozialwissenschaften sowie anderer Anwendungsdisziplinen und
als Einfuhrung fur Studenten der Statistik.
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.
Das Werk gibt eine Einfuhrung in die Kategoriale Regression, die
auch fur Anwender sehr gut geeignet ist. Neben
wirtschaftswissenschaftlichen Beispielen, die zahlenmassig
dominieren, finden sich Beispiele aus Biometrie, der medizinischen
Statistik, Psychologie und Demographie. Aus dem Inhalt: Einfuhrung.
Logistische Regression und Logit-Modell fur binare abhangige
Grossen. Schatzung, Modellanpassung und Einflussgrossen.
Multinominale Modelle fur ungeordnete Kategorien. Regression mit
ordinaler abhangiger Variable. Zahldaten und die Analyse von
Kontingenztafeln: das loglineare Modell. Nonparametrische
Regression I: Glattungsverfahren. Nonparametrische Regression II:
Klassifikations- und Regressionsbaume. Kategoriale Prognose und
Diskriminanzanalyse. Elemente der Schatz- und Testtheorie. Anhang."
Das Buch gibt eine Einfuhrung und einen Einblick in Latent
Trait-Modelle zum Paarvergleich mit ordinaler Reaktionsvariabler.
Zahlreiche neue Ergebnisse werden unter dem Gesichtspunkt der
Praktikabilitat sowie der messtheoretischen Fundierung
vorgestellt."
This book introduces basic and advanced concepts of categorical
regression with a focus on the structuring constituents of
regression, including regularization techniques to structure
predictors. In addition to standard methods such as the logit and
probit model and extensions to multivariate settings, the author
presents more recent developments in flexible and high-dimensional
regression, which allow weakening of assumptions on the structuring
of the predictor and yield fits that are closer to the data. A
generalized linear model is used as a unifying framework whenever
possible in particular parametric models that are treated within
this framework. Many topics not normally included in books on
categorical data analysis are treated here, such as nonparametric
regression; selection of predictors by regularized estimation
procedures; ternative models like the hurdle model and
zero-inflated regression models for count data; and non-standard
tree-based ensemble methods, which provide excellent tools for
prediction and the handling of both nominal and ordered categorical
predictors. The book is accompanied an R package that contains data
sets and code for all the examples.
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