Ludwig Fahrmeir – författare & böcker

Del 36 - Oxford Statistical Science Series

Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data

Inbunden, Engelska, 2011

1 683 kr

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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.

Multivariate Statistical Modelling Based on Generalized Linear Models

AvLudwig Fahrmeir,Gerhard Tutz

Inbunden, Engelska, 2001

2 688 kr

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Since our first edition of this book, many developments in statistical mod elling based on generalized linear models have been published, and our primary aim is to bring the book up to date. Naturally, the choice of these recent developments reflects our own teaching and research interests. The new organization parallels that of the first edition. We try to motiv ate and illustrate concepts with examples using real data, and most data sets are available on http:/ fwww. stat. uni-muenchen. de/welcome_e. html, with a link to data archive. We could not treat all recent developments in the main text, and in such cases we point to references at the end of each chapter. Many changes will be found in several sections, especially with those connected to Bayesian concepts. For example, the treatment of marginal models in Chapter 3 is now current and state-of-the-art. The coverage of nonparametric and semiparametric generalized regression in Chapter 5 is completely rewritten with a shift of emphasis to linear bases, as well as new sections on local smoothing approaches and Bayesian inference. Chapter 6 now incorporates developments in parametric modelling of both time series and longitudinal data. Additionally, random effect models in Chapter 7 now cover nonparametric maximum likelihood and a new section on fully Bayesian approaches. The modifications and extensions in Chapter 8 reflect the rapid development in state space and hidden Markov models.

Del 78 - Lecture Notes in Statistics

Advances in GLIM and Statistical Modelling

Proceedings of the GLIM92 Conference and the 7th International Workshop on Statistical Modelling, Munich, 13–17 July 1992

AvLudwig Fahrmeir,Brian Francism. fl.

Häftad, Engelska, 1992

1 079 kr

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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.

Multivariate Statistical Modelling Based on Generalized Linear Models

AvLudwig Fahrmeir,Gerhard Tutz

Häftad, Engelska, 2010

2 688 kr

Skickas inom 10-15 vardagar

Since our first edition of this book, many developments in statistical mod elling based on generalized linear models have been published, and our primary aim is to bring the book up to date. Naturally, the choice of these recent developments reflects our own teaching and research interests. The new organization parallels that of the first edition. We try to motiv ate and illustrate concepts with examples using real data, and most data sets are available on http:/ fwww. stat. uni-muenchen. de/welcome_e. html, with a link to data archive. We could not treat all recent developments in the main text, and in such cases we point to references at the end of each chapter. Many changes will be found in several sections, especially with those connected to Bayesian concepts. For example, the treatment of marginal models in Chapter 3 is now current and state-of-the-art. The coverage of nonparametric and semiparametric generalized regression in Chapter 5 is completely rewritten with a shift of emphasis to linear bases, as well as new sections on local smoothing approaches and Bayesian inference. Chapter 6 now incorporates developments in parametric modelling of both time series and longitudinal data. Additionally, random effect models in Chapter 7 now cover nonparametric maximum likelihood and a new section on fully Bayesian approaches. The modifications and extensions in Chapter 8 reflect the rapid development in state space and hidden Markov models.

Advances in GLIM and Statistical Modelling

Proceedings of the GLIM92 Conference and the 7th International Workshop on Statistical Modelling, Munich, 13–17 July 1992

AvGerhard Tutz,Robert Gilchristm. fl.

E-bok

PDF, Engelska, 2012

1 416 kr

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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.

Multivariate Statistical Modelling Based on Generalized Linear Models

AvGerhard Tutz,Ludwig Fahrmeir

E-bok

PDF, Engelska, 2013

3 473 kr

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Since our first edition of this book, many developments in statistical mod elling based on generalized linear models have been published, and our primary aim is to bring the book up to date. Naturally, the choice of these recent developments reflects our own teaching and research interests. The new organization parallels that of the first edition. We try to motiv ate and illustrate concepts with examples using real data, and most data sets are available on http:/ fwww. stat. uni-muenchen. de/welcome_e. html, with a link to data archive. We could not treat all recent developments in the main text, and in such cases we point to references at the end of each chapter. Many changes will be found in several sections, especially with those connected to Bayesian concepts. For example, the treatment of marginal models in Chapter 3 is now current and state-of-the-art. The coverage of nonparametric and semiparametric generalized regression in Chapter 5 is completely rewritten with a shift of emphasis to linear bases, as well as new sections on local smoothing approaches and Bayesian inference. Chapter 6 now incorporates developments in parametric modelling of both time series and longitudinal data. Additionally, random effect models in Chapter 7 now cover nonparametric maximum likelihood and a new section on fully Bayesian approaches. The modifications and extensions in Chapter 8 reflect the rapid development in state space and hidden Markov models.

Multivariate Statistical Modelling Based on Generalized Linear Models

AvGerhard Tutz,Ludwig Fahrmeir

E-bok

PDF, Engelska, 2013

1 140 kr

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Classical statistical models for regression, time series and longitudinal data provide well-established tools for approximately normally distributed vari ables. Enhanced by the availability of software packages these models dom inated the field of applications for a long time. With the introduction of generalized linear models (GLM) a much more flexible instrument for sta tistical modelling has been created. The broad class of GLM''s includes some of the classicallinear models as special cases but is particularly suited for categorical discrete or nonnegative responses. The last decade has seen various extensions of GLM''s: multivariate and multicategorical models have been considered, longitudinal data analysis has been developed in this setting, random effects and nonparametric pre dictors have been included. These extended methods have grown around generalized linear models but often are no longer GLM''s in the original sense. The aim of this book is to bring together and review a large part of these recent advances in statistical modelling. Although the continuous case is sketched sometimes, thoughout the book the focus is on categorical data. The book deals with regression analysis in a wider sense including not only cross-sectional analysis but also time series and longitudinal data situations. We do not consider problems of symmetrical nature, like the investigation of the association structure in a given set of variables. For example, log-linear models for contingency tables, which can be treated as special cases of GLM''s are totally omitted. The estimation approach that is primarily considered in this book is likelihood-based.

Multivariate statistische Verfahren

AvLudwig Fahrmeir,Alfred Hamerlem. fl.

Inbunden, Tyska, 1996

3 756 kr

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Multivariate statistische Verfahren

AvGerhard Tutz,Alfred Hamerlem. fl.

E-bok

PDF, Tyska, 2015

3 440 kr

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Arbeitsbuch Statistik

AvStefan Lang,Angelika Caputom. fl.

E-bok

PDF, Tyska, 2006

159 kr

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Dieses Arbeitsbuch ergänzt perfekt das Lehrbuch Fahrmeir/Künstler/Pigeot/Tutz: Statistik - Der Weg zur Datenanalyse. Es enthält die Lösungen zu den dort gestellten Aufgaben. Darüber hinaus bietet es eine Reihe weiterer Aufgaben inklusive Lösungen und Computerübungen mit realen Daten. Es dient damit der Vertiefung und der Einübung des im Lehrbuch vermittelten Stoffes zur Wahrscheinlichkeitsrechnung, deskriptiven und induktiven Statistik. Die vierte Auflage ist an das Eurozeitalter angepaßt und enthält viele Verbesserungen.

Kreditrisikomessung

Statistische Grundlagen, Methoden und Modellierung

AvAndreas Henking,Christian Bluhmm. fl.

Inbunden, Tyska, 2006

936 kr

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Jeder Kredit birgt für den Kreditgeber ein Risiko, da es unsicher ist, ob der Kreditnehmer seinen Zahlungsverpflichtungen nachkommen wird. 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. Dieses Buch schließt die Lücke zwischen statistischer Grundlagenliteratur und mathematisch anspruchsvollen Werken zur Modellierung von Kreditrisiken. Es bietet einen Einstieg in die Kreditrisikomessung und die dafür notwendige Statistik. Ausgehend von den wichtigsten Begriffen zum Kreditrisiko werden deren statistische Analoga beschrieben. Das Buch stellt die relevanten statistischen Verteilungen dar und gibt eine Einführung in stochastische Prozesse, Portfoliomodelle und Score- bzw. Ratingmodelle. Mit zahlreichen praxisnahen Beispielen ist es der ideale Einstieg in die Kreditrisikomessung für Praktiker und Quereinsteiger.

Kreditrisikomessung

Statistische Grundlagen, Methoden und Modellierung

AvLudwig Fahrmeir,Christian Bluhmm. fl.

E-bok

PDF, Tyska, 2006

950 kr

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Jeder Kredit birgt für den Kreditgeber ein Risiko, da es unsicher ist, ob der Kreditnehmer seinen Zahlungsverpflichtungen nachkommen wird. 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.

Dieses Buch schließt die Lücke zwischen statistischer Grundlagenliteratur und mathematisch anspruchsvollen Werken zur Modellierung von Kreditrisiken. Es bietet einen Einstieg in die Kreditrisikomessung und die dafür notwendige Statistik. Ausgehend von den wichtigsten Begriffen zum Kreditrisiko werden deren statistische Analoga beschrieben. Das Buch stellt die relevanten statistischen Verteilungen dar und gibt eine Einführung in stochastische Prozesse, Portfoliomodelle und Score- bzw. Ratingmodelle. Mit zahlreichen praxisnahen Beispielen ist es der ideale Einstieg in die Kreditrisikomessung für Praktiker und Quereinsteiger.

Regression

Modelle, Methoden und Anwendungen

AvStefan Lang,Thomas Kneibm. fl.

E-bok

PDF, Tyska, 2007

348 kr

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In dieser Einführung werden erstmals klassische Regressionsansätze und moderne nicht- und semiparametrische Methoden in einer integrierten, einheitlichen und anwendungsorientierten Form beschrieben. Die Darstellung wendet sich an Studierende der Statistik in Wahl- und Hauptfach sowie an empirisch-statistisch und interdisziplinär arbeitende Wissenschaftler und Praktiker, zum Beispiel in Wirtschafts- und Sozialwissenschaften, Bioinformatik, Biostatistik, Ökonometrie, Epidemiologie. Die praktische Anwendung der vorgestellten Konzepte und Methoden wird anhand ausführlich vorgestellter Fallstudien demonstriert, um dem Leser die Analyse eigener Fragestellungen zu ermöglichen.

Statistik

Der Weg zur Datenanalyse

AvGerhard Tutz,Iris Pigeotm. fl.

E-bok

PDF, Tyska, 2006

538 kr

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Das Buch bietet eine integrierte Darstellung der deskriptiven Statistik, moderner Methoden der explorativen Datenanalyse und der induktiven Statistik, einschließlich der Regressions- und Varianzanalyse. Die Darstellung ist auf inhaltliche Motivation, Interpretation und Verständnis der Methoden ausgerichtet. Zahlreiche Beispiele mit realen Daten und Graphiken veranschaulichen den Text. Texthervorhebungen zentraler Aspekte und Stichwörter am Rand erhöhen die Lesbarkeit und Übersichtlichkeit. Das Buch eignet sich als vorlesungsbegleitender Text, aber auch zum Selbststudium für Studenten aus den Bereichen der Wirtschafts- und Sozialwissenschaften, anderen Anwendungsdisziplinen der Statistik sowie als Einführungstext für Studenten der Statistik.

Statistik

Der Weg zur Datenanalyse

AvGerhard Tutz,Iris Pigeotm. fl.

E-bok

PDF, Tyska, 2007

326 kr

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Das Buch bietet eine integrierte Darstellung der deskriptiven Statistik, moderner Methoden der explorativen Datenanalyse und der induktiven Statistik, einschließlich der Regressions- und Varianzanalyse. Die Darstellung ist auf inhaltliche Motivation, Interpretation und Verständnis der Methoden ausgerichtet. Zahlreiche Beispiele mit realen Daten und Graphiken veranschaulichen den Text. Texthervorhebungen zentraler Aspekte und Stichwörter am Rand erhöhen die Lesbarkeit und Übersichtlichkeit. Das Buch eignet sich als vorlesungsbegleitender Text, aber auch zum Selbststudium für Studenten aus den Bereichen der Wirtschafts- und Sozialwissenschaften, anderen Anwendungsdisziplinen der Statistik sowie als Einführungstext für Studenten der Statistik.

Arbeitsbuch Statistik

AvAngelika Caputo,Ludwig Fahrmeirm. fl.

Häftad, Tyska, 2008

489 kr

Skickas inom 5-8 vardagar

Dieses Arbeitsbuch ergänzt perfekt das Lehrbuch Fahrmeir/Künstler/Pigeot/Tutz: Statistik - Der Weg zur Datenanalyse. Es dient damit der Vertiefung und der Einübung des im Lehrbuch vermittelten Stoffes zur Wahrscheinlichkeitsrechnung, deskriptiven und induktiven Statistik.

Arbeitsbuch Statistik

AvGerhard Tutz,Iris Pigeot-Kublerm. fl.

E-bok

PDF, Tyska, 2008

412 kr

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Dieses Arbeitsbuch ergänzt perfekt das Lehrbuch Fahrmeir/Künstler/Pigeot/Tutz: Statistik - Der Weg zur Datenanalyse. Es bietet eine Fülle von Aufgaben inklusive Lösungen und Computerübungen mit realen Daten. Es dient damit der Vertiefung und der Einübung des im Lehrbuch vermittelten Stoffes zur Wahrscheinlichkeitsrechnung, deskriptiven und induktiven Statistik. Die 5. Auflage enthält eine Reihe neuer Aufgaben, die hauptsächlich in Klausuren verwendet wurden.

Regression

Modelle, Methoden und Anwendungen

AvLudwig Fahrmeir,Thomas Kneibm. fl.

Häftad, Tyska, 2009

833 kr

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In dieser Einführung werden erstmals klassische Regressionsansätze und moderne nicht- und semiparametrische Methoden in einer integrierten, einheitlichen und anwendungsorientierten Form beschrieben. Die Darstellung wendet sich an Studierende der Statistik in Wahl- und Hauptfach sowie an empirisch-statistisch und interdisziplinär arbeitende Wissenschaftler und Praktiker, zum Beispiel in Wirtschafts- und Sozialwissenschaften, Bioinformatik, Biostatistik, Ökonometrie, Epidemiologie. Die praktische Anwendung der vorgestellten Konzepte und Methoden wird anhand ausführlich vorgestellter Fallstudien demonstriert, um dem Leser die Analyse eigener Fragestellungen zu ermöglichen.

Regression

Modelle, Methoden und Anwendungen

AvStefan Lang,Thomas Kneibm. fl.

E-bok

PDF, Tyska, 2009

823 kr

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In dieser Einführung werden erstmals klassische Regressionsansätze und moderne nicht- und semiparametrische Methoden in einer integrierten, einheitlichen und anwendungsorientierten Form beschrieben. Die Darstellung wendet sich an Studierende der Statistik in Wahl- und Hauptfach sowie an empirisch-statistisch und interdisziplinär arbeitende Wissenschaftler und Praktiker, zum Beispiel in Wirtschafts- und Sozialwissenschaften, Bioinformatik, Biostatistik, Ökonometrie, Epidemiologie. Die praktische Anwendung der vorgestellten Konzepte und Methoden wird anhand ausführlich vorgestellter Fallstudien demonstriert, um dem Leser die Analyse eigener Fragestellungen zu ermöglichen.

Regression

Models, Methods and Applications

AvLudwig Fahrmeir,Thomas Kneibm. fl.

Inbunden, Engelska, 2013

1 807 kr

Skickas inom 5-8 vardagar

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.

Regression

Models, Methods and Applications

AvBrian Marx,Stefan Langm. fl.

E-bok

PDF, Engelska, 2013

1 833 kr

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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.

Arbeitsbuch Statistik

AvStefan Lang,Angelika Caputom. fl.

E-bok

PDF, Tyska, 2013

538 kr

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Dieses Arbeitsbuch ergänzt perfekt das Lehrbuch Fahrmeir/Künstler/Pigeot/Tutz: Statistik - Der Weg zur Datenanalyse. Es enthält die Lösungen zu den dort gestellten Aufgaben. Darüber hinaus bietet es eine Reihe weiterer Aufgaben inklusive Lösungen und Computerübungen mit realen Daten. Es dient damit der Vertiefung und der Einübung des im Lehrbuch vermittelten Stoffes zur Wahrscheinlichkeitsrechnung, deskriptiven und induktiven Statistik.

Arbeitsbuch Statistik

AvStefan Lang,Angelika Caputom. fl.

E-bok

PDF, Tyska, 2013

538 kr

Läs direkt efter köp

Dieses Arbeitsbuch ergänzt perfekt das Lehrbuch Fahrmeir/Künstler/Pigeot/Tutz: Statistik - Der Weg zur Datenanalyse. Es enthält die Lösungen zu den dort gestellten Aufgaben. Darüber hinaus bietet es eine Reihe weiterer Aufgaben inklusive Lösungen und Computerübungen mit realen Daten. Es dient damit der Vertiefung und der Einübung des im Lehrbuch vermittelten Stoffes zur Wahrscheinlichkeitsrechnung, deskriptiven und induktiven Statistik.

Models, Methods and Applications

AvLudwig Fahrmeir,Thomas Kneibm. fl.

Inbunden, Engelska, 2022

1 723 kr

Skickas inom 10-15 vardagar

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.

Regression

Models, Methods and Applications

AvBrian D. Marx,Stefan Langm. fl.

E-bok

Engelska, 2022

1 733 kr

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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.