{"product_id":"multivariate-statistical-modelling-based-on-generalized-linear-models","title":"Multivariate Statistical Modelling Based on Generalized Linear Models","description":"\u003cp\u003eOur review of Multivariate Statistical Modelling Based on Generalized Linear Models finds it a focused, scholarly resource for statisticians and graduate students who need a current, example-driven treatment of generalized linear model extensions. The book updates the earlier edition by incorporating recent developments, particularly in areas linked to \u003cstrong\u003eBayesian concepts\u003c\/strong\u003e, and emphasizes motivation through real data sets that the authors make available online. Readers seeking thorough exposition and applied examples will find the content rewarding; those needing an introductory textbook or light overview should look elsewhere.\u003c\/p\u003e\n\n\u003ch2\u003eKey Features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eUpdated coverage:\u003c\/strong\u003e The text brings the original edition up to date by including recent developments in statistical modelling based on generalized linear models, helping readers stay current with the field.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExample-driven approach:\u003c\/strong\u003e Concepts are motivated and illustrated with real data sets, which supports practical understanding and reproducible study.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBayesian extensions:\u003c\/strong\u003e Several sections incorporate changes connected to Bayesian ideas, giving readers modern perspectives on inference within the generalized linear modelling framework.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStructured organization:\u003c\/strong\u003e The new edition preserves the organization of the first edition, making it easy for prior users to transition and locate familiar topics.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReference pointers:\u003c\/strong\u003e When recent developments are not treated fully in the main text, the book directs readers to references at the end of chapters for deeper study.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eData availability:\u003c\/strong\u003e Most data sets used for examples are provided online via the authors' university link, enabling hands-on replication of analyses.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eWho It's For\u003c\/h2\u003e\n\u003cp\u003eThis book is best for advanced undergraduates, graduate students, and researchers in statistics, biostatistics, and related fields who need a rigorous treatment of multivariate modelling built on generalized linear models. Practitioners who apply GLMs and want a modern, example-rich reference will appreciate the mix of theory and applied data.\u003c\/p\u003e\n\u003cp\u003eIt is not aimed at readers looking for a gentle introduction or an elementary textbook; newcomers to statistical modelling without prior exposure to generalized linear models or multivariate techniques may find the material dense and should seek a more introductory-level resource first.\u003c\/p\u003e\n\n\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThoroughly updated content provides current developments in GLM-based multivariate modelling.\u003c\/li\u003e\n\u003cli\u003eConcrete, real-data examples help readers connect theory to practice and replicate analyses using the provided data archive.\u003c\/li\u003e\n\u003cli\u003eChapters include references for deeper exploration where the main text does not cover all recent advances.\u003c\/li\u003e\n\u003cli\u003eContinuity with the first edition makes it convenient for returning readers to follow new material.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe book assumes a solid statistical background and may be challenging for readers who need an introductory treatment.\u003c\/li\u003e\n\u003cli\u003eNot all recent developments are developed fully in the main text, requiring readers to consult referenced papers for some topics.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eMultivariate Statistical Modelling Based on Generalized Linear Models\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSeries\u003c\/td\u003e\n\u003ctd\u003eSpringer Series in Statistics\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAuthor\u003c\/td\u003e\n\u003ctd\u003eL. Fahrmeir\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEdition focus\u003c\/td\u003e\n\u003ctd\u003eUpdated developments and Bayesian-related content\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eExamples\u003c\/td\u003e\n\u003ctd\u003eReal data sets with online access via the authors' university link\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eReferences\u003c\/td\u003e\n\u003ctd\u003eEnd-of-chapter pointers to further literature\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eOur Verdict\u003c\/h2\u003e\n\u003cp\u003eFor readers with a background in statistical theory who need an updated, example-rich reference on multivariate modelling using generalized linear models, this edition is a strong choice that balances theory and applications. It represents good value for graduate students and researchers because of its updated coverage, practical data examples, and clear direction to further literature.\u003c\/p\u003e\n\n\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDoes this edition include practical data sets?\u003c\/strong\u003e\u003cbr\u003eYes. Most of the data sets used for examples are made available online through the authors' university data archive to allow replication.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs this book suitable for beginners?\u003c\/strong\u003e\u003cbr\u003eNo. The book assumes prior knowledge of generalized linear models and multivariate methods; beginners should start with a more introductory text.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAre recent developments covered in depth?\u003c\/strong\u003e\u003cbr\u003eThe book updates many recent developments and highlights Bayesian-related changes, but it points to references at the end of chapters when a topic is not treated fully in the main text.\u003c\/p\u003e","brand":"L. 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