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Generalized Linear Models With Examples in R de Peter K. Dunn

Generalized Linear Models With Examples in R: Springer Texts in Statistics

Autor Peter K. Dunn, Gordon K. Smyth
en Limba Engleză Hardback – 11 noi 2018

Descoperim în Generalized Linear Models With Examples in R o resursă pedagogică riguroasă, publicată în seria Springer Texts in Statistics, care reușește să echilibreze teoria matematică cu practica computațională. Această primă ediție din 2018 se distinge prin utilizarea nativă a limbajului R, oferind cititorului acces la pachetul GLMsData, ce include aproape 100 de seturi de date pentru exerciții aplicate. Suntem de părere că volumul este esențial pentru tranziția de la modelele liniare clasice la cele generalizate, acoperind variabile de răspuns care nu urmează o distribuție normală, precum numărătorile sau proporțiile.

Structura cărții este progresivă: începe cu fundamentele regresiei liniare și diagnosticul acestora, avansând rapid către metoda verosimilității maxime și structura specifică a modelelor GLM. Subliniem includerea unor teme avansate, rareori tratate în manualele introductive, cum ar fi funcțiile de varianță putere, testele scorului de verosimilitate și asimptotica de dispersie mică. Această profunzime teoretică completează perspectiva oferită de Beyond Multiple Linear Regression, adăugând un fundament matematic mai dens și tehnici de diagnosticare mai sofisticate pentru modelele Poisson și binomiale.

În contextul operei autorului Peter K. Dunn, lucrarea de față rafinează conceptele metodologice prezentate în Scientific Research and Methodology, mutând accentul de la designul general de cercetare către execuția statistică propriu-zisă. Dacă în lucrările anterioare accentul cădea pe procesul de colectare și sumarizare a datelor, aici Peter K. Dunn și Gordon K. Smyth oferă un instrumentar complet pentru analiză, transformând teoria în competențe practice prin exemple cross-referențiate și scripturi R gata de utilizat.

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Specificații

ISBN-13: 9781441901170
ISBN-10: 1441901175
Pagini: 584
Ilustrații: XX, 562 p. 115 illus.
Dimensiuni: 160 x 241 x 37 mm
Greutate: 1.03 kg
Ediția:1st ed. 2018
Editura: Humana
Colecția Springer Texts in Statistics
Seria Springer Texts in Statistics

Locul publicării:New York, NY, United States

De ce să citești această carte

Această carte se adresează studenților și cercetătorilor care doresc să stăpânească modelele liniare generalizate folosind R. Cititorul câștigă nu doar o înțelegere teoretică a distribuțiilor non-normale, ci și abilitatea practică de a modela date complexe prin exemple reale. Este o recomandare certă pentru cei care au depășit nivelul regresiei simple și au nevoie de un toolkit statistic robust pentru date de tip numărătoare, proporții sau cantități pozitive.

Despre autor

Peter K. Dunn este un academician recunoscut pentru contribuțiile sale în educația statistică și metodologia cercetării, fiind implicat în dezvoltarea de resurse care fac statistica accesibilă studenților din științe și sănătate. Gordon K. Smyth este un statistician de renume, membru al Walter and Eliza Hall Institute of Medical Research, cunoscut la nivel mondial pentru dezvoltarea unor metode statistice inovatoare aplicate în bioinformatică și genomică. Expertiza sa în dezvoltarea de software statistic și pachete R conferă acestei lucrări o autoritate tehnică deosebită, asigurând relevanța metodelor prezentate pentru analiza datelor moderne.

Descriere scurtă

This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities.


The book is designed with the student in mind, making it suitable for self-study or a structured course. Beginning with an introduction to linear regression, the book also devotes time to advanced topics not typically included in introductory textbooks. It features chapter introductions and summaries, clear examples, and many practice problems, all carefully designed to balance theory and practice. The text also provides a working knowledge of applied statistical practice through the extensive use of R, which is integrated into the text. 

Other features include:
•             Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals
•             Nearly 100 data sets in the companion R package GLMsData
•             Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session

Cuprins

Statistical models.- Linear regression models.-  Linear regression models: diagnostics and model-building.- Beyond linear regression: the method of maximum likelihood.- Generalized linear models: structure.- Generalized linear models: estimation.- Generalized linear models: inference.- Generalized linear models: diagnostics.- Models for proportions: binomial GLMs.- Models for counts: Poisson and negative binomial GLMs.- Positive continuous data: gamma and inverse Gaussian GLMs.- Tweedie GLMs.- Extra problems.- Appendix A: Using R for data analysis.- Appendix B: The GLMsData package.- Index: Data sets.- Index: R commands.- Index: General Topics. 

Recenzii

“This is a great book … . The book comprehensively covers almost everything you need to know or teach in this area. This book is an invaluable reference either as a classroom text or for the researcher’s bookshelf.” (Pablo Emilio Verde, ISCB News, iscb.info, Issue 69, July, 2020)

“I congratulate the authors for making an important contribution in this field. … the book represents an excellent and very comprehensible introduction into the world of generalized linear models and is recommended for all readers who are looking for a practical introduction to this topic using R.” (Dominic Edelmann, Biometrical Journal, Vol. 62, 2020)

“The book is targeted at students and notes it is appropriate for graduate students. It is also useful to the junior statistician needing to learn how to work a model they are unfamiliar with. The practicing and experienced statistician can use this as a quick reference for working a model they may have forgotten the specific of.” (James P. Howard II, zbMath 1416.62020, 2019)

Notă biografică

Peter K. Dunn is Associate Professor in the Faculty of Science, Health, Education and Engineering at the University of the Sunshine Coast. His work focuses on mathematical statistics, in particular generalized linear models. He has developed methods for accurate numerical evaluation of the densities of the Tweedie distributions, leading to a better understanding of these distributions. An engaging teacher, Dunn is the recipient of an Australian Office of Learning and Teaching citation. He has also won several conference paper prizes, including the EJ Pitman Prize at the Australian Statistics Conference.  He is a member of the Statistical Society of Australia Inc. and the Australian Mathematics Society. 


Gordon K. Smyth is Head of the Bioinformatics Division at the Walter and Eliza Hall Institute of Medical Research and Honorary Professor of Mathematics & Statistics at The University of Melbourne. He has published research on generalized linear models and statistical computing for over 30 years and is the author of several popular R packages. In recent years, he has particularly promoted the use of generalized linear models to model data from genomic sequencing technologies.

Textul de pe ultima copertă

This textbook presents an introduction to multiple linear regression, providing real-world data sets and practice problems. A practical working knowledge of applied statistical practice is developed through the use of these data sets and numerous case studies. The authors include a set of practice problems both at the end of each chapter and at the end of the book. Each example in the text is cross-referenced with the relevant data set, so that readers can load the data and follow the analysis in their own R sessions. The balance between theory and practice is evident in the list of problems, which vary in difficulty and purpose.

This book is designed with teaching and learning in mind, featuring chapter introductions and summaries, exercises, short answers, and simple, clear examples. Focusing on the connections between generalized linear models (GLMs) and linear regression, the book also references advanced topics and tools that have not typically been included in introductions to GLMs to date, such as Tweedie family distributions with power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, and randomized quantile residuals. In addition, the authors introduce the new R code package, GLMsData, created specifically for this book. Generalized Linear Models with Examples in R balances theory with practice, making it ideal for both introductory and graduate-level students who have a basic knowledge of matrix algebra, calculus, and statistics.  

Caracteristici

This book eases students into GLMs and motivates the need for GLMs by starting with regression. A practical working knowledge of good applied statistical practice is developed through the use of these real data sets and numerous case studies Each example in the text is cross-referenced with the relevant data set so that readers can load this data to follow the analysis in their own R session.