Generalized Linear Models and Extensions: Books (35 results)

Paperback. Condition: Very Good. Generalized Linear Models and Extensions, Second Edition provides a comprehensive overview of the nature and scope of generalized linear models (GLMs) and of the major changes to the basic GLM algorithm that allow modeling of data that violate GLM distributional assumptions. Deftly balancing theory and application, the book stands out in its coverage of the derivation of the GLM families and their foremost links, while also guiding readers in the application of the various models to real data. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, generalized Poisson, and generalized binomial models. The book also includes a substantially expanded discussion of both proportional-odds and generalized ordered models, making it easy for readers to use these models in their own research.…

Paperback. Condition: Good. Generalized Linear Models and Extensions, Second Edition provides a comprehensive overview of the nature and scope of generalized linear models (GLMs) and of the major changes to the basic GLM algorithm that allow modeling of data that violate GLM distributional assumptions. Deftly balancing theory and application, the book stands out in its coverage of the derivation of the GLM families and their foremost links, while also guiding readers in the application of the various models to real data. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, generalized Poisson, and generalized binomial models. The book also includes a substantially expanded discussion of both proportional-odds and generalized ordered models, making it easy for readers to use these models in their own research.…

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Paperback. Condition: Very Good. Generalized Linear Models and Extensions, Second Edition provides a comprehensive overview of the nature and scope of generalized linear models (GLMs) and of the major changes to the basic GLM algorithm that allow modeling of data that violate GLM distributional assumptions. Deftly balancing theory and application, the book stands out in its coverage of the derivation of the GLM families and their foremost links, while also guiding readers in the application of the various models to real data. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, generalized Poisson, and generalized binomial models. The book also includes a substantially expanded discussion of both proportional-odds and generalized ordered models, making it easy for readers to use these models in their own research.…

Paperback. Condition: Good. Generalized Linear Models and Extensions, Second Edition provides a comprehensive overview of the nature and scope of generalized linear models (GLMs) and of the major changes to the basic GLM algorithm that allow modeling of data that violate GLM distributional assumptions. Deftly balancing theory and application, the book stands out in its coverage of the derivation of the GLM families and their foremost links, while also guiding readers in the application of the various models to real data. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, generalized Poisson, and generalized binomial models. The book also includes a substantially expanded discussion of both proportional-odds and generalized ordered models, making it easy for readers to use these models in their own research.…

Condition: acceptable. It's been through some chapters of life! Expect visible wearâ"creases, notes, highlights, maybe even a splash of water here and there. Perfect for readers who love a book with history.

Paperback. Condition: Very Good. 3. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Condition: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condition: good. Book shows general signs of use and handling. May have light wear on the cover or edges and minimal writing or highlighting. Binding remains tight, and pages are clean and readable.

Paperback. Condition: Very Good. 4. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

paperback. Condition: New.

XIV, 245 Seiten. Gr.-8vo. Kartoniert. Ehemaliges Bibliotheksexemplar, im Impressum gestempelt u. mit Rückenschild. Leichte Gebrauchsspuren. Sprache: Deutsch Gewicht in Gramm: 650.

Condition: Poor. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In poor condition, suitable as a reading copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:1881228606. …

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a wide range of topics to address the needs of several groups of users of rapidly growing methods of generalized linear models. Since the introduction of the idea of generalized linear models (GLM) in early seventies, during the past four decades the modelling of statistical data have experienced a major transformation from linear models based on normality assumption to a more flexible unified approach of generalized linear models.The number of readers and users of generalized linear models have increased manifold. In addition, the use of generalized linear models has expanded in many new fields of applications where statistical models are being employed at an increasing rate. It is important to note here that the learners and users of GLM have a widely varied background in different disciplines. Considering these pressing needs, this book focuses on: (i) upper undergraduate and graduate level students in need of a thorough understanding about the basic concepts of generalized linear models along with appropriate applications; (ii) researchers and users in need of advanced generalized linear models for analysing bivariate or multivariate data stemming from longitudinal or repeated measures data; and (iii) new challenges to analyse big data where the traditional techniques fail to provide any reasonable modelling strategy. In other words, this book starts with a thorough background of the generalized linear models for the new learners, then provides multivariate extensions to advanced level techniques for researchers and users in various disciplines, and finally some innovative modelling strategies are introduced using generalized linear models in the emerging field of big data analytics. It provides materials for new learners, for users/researchers who are in need of more advanced techniques and also strategies for employing linear models in big data analytics. Hence, techniques of generalized linear models will be presented in the proposed book covering the needs of new learners, users of advanced techniques, researchers in need of statistical modelling of any data type and users of big data analytics wanting to increase predictive accuracy of classification and regression tree techniques. …

Condition: New. pp. 598.

Paperback. Condition: Good. This book presents a thorough examination of generalized linear model (GLM) estimation methods as well as the derivation of all major GLM families. Examined families include Gaussian, gamma, inverse Gaussian, binomial, Poisson, geometric, and negative binomial. The text also contains various models that have been developed on the basis of GLM theory, including GAM, ordered binomial models, multinomial logit and probit models, GEE and other quasi-likelihood models, fixed and random effects models, and random intercept and random parameter models. Using Stata, the book offers numerous examples to assist you in applying the models to your own data situations.…

Paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Paperback. Condition: Good. This book presents a thorough examination of generalized linear model (GLM) estimation methods as well as the derivation of all major GLM families. Examined families include Gaussian, gamma, inverse Gaussian, binomial, Poisson, geometric, and negative binomial. The text also contains various models that have been developed on the basis of GLM theory, including GAM, ordered binomial models, multinomial logit and probit models, GEE and other quasi-likelihood models, fixed and random effects models, and random intercept and random parameter models. Using Stata, the book offers numerous examples to assist you in applying the models to your own data situations.…

Taschenbuch. Condition: Neu. Generalized Linear Models and Extensions | M. Ataharul Islam (u. a.) | Taschenbuch | xvii | Englisch | 2026 | Springer | EAN 9789819648504 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a wide range of topics to address the needs of several groups of users of rapidly growing methods of generalized linear models. Since the introduction of the idea of generalized linear models (GLM) in early seventies, during the past four decades the modelling of statistical data have experienced a major transformation from linear models based on normality assumption to a more flexible unified approach of generalized linear models.The number of readers and users of generalized linear models have increased manifold. In addition, the use of generalized linear models has expanded in many new fields of applications where statistical models are being employed at an increasing rate. It is important to note here that the learners and users of GLM have a widely varied background in different disciplines. Considering these pressing needs, this book focuses on: (i) upper undergraduate and graduate level students in need of a thorough understanding about the basic concepts of generalized linear models along with appropriate applications; (ii) researchers and users in need of advanced generalized linear models for analysing bivariate or multivariate data stemming from longitudinal or repeated measures data; and (iii) new challenges to analyse big data where the traditional techniques fail to provide any reasonable modelling strategy. In other words, this book starts with a thorough background of the generalized linear models for the new learners, then provides multivariate extensions to advanced level techniques for researchers and users in various disciplines, and finally some innovative modelling strategies are introduced using generalized linear models in the emerging field of big data analytics. It provides materials for new learners, for users/researchers who are in need of more advanced techniques and also strategies for employing linear models in big data analytics. Hence, techniques of generalized linear models will be presented in the proposed book covering the needs of new learners, users of advanced techniques, researchers in need of statistical modelling of any data type and users of big data analytics wanting to increase predictive accuracy of classification and regression tree techniques.…

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: new. Questo è un articolo print on demand.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a wide range of topics to address the needs of several groups of users of rapidly growing methods of generalized linear models. Since the introduction of the idea of generalized linear models (GLM) in early seventies, during the past four decades the modelling of statistical data have experienced a major transformation from linear models based on normality assumption to a more flexible unified approach of generalized linear models. The number of readers and users of generalized linear models have increased manifold. In addition, the use of generalized linear models has expanded in many new fields of applications where statistical models are being employed at an increasing rate. It is important to note here that the learners and users of GLM have a widely varied background in different disciplines. Considering these pressing needs, this book focuses on: (i) upper undergraduate and graduate level students in need of a thorough understanding about the basic concepts of generalized linear models along with appropriate applications; (ii) researchers and users in need of advanced generalized linear models for analysing bivariate or multivariate data stemming from longitudinal or repeated measures data; and (iii) new challenges to analyse big data where the traditional techniques fail to provide any reasonable modelling strategy. In other words, this book starts with a thorough background of the generalized linear models for the new learners, then provides multivariate extensions to advanced level techniques for researchers and users in various disciplines, and finally some innovative modelling strategies are introduced using generalized linear models in the emerging field of big data analytics. It provides materials for new learners, for users/researchers who are in need of more advanced techniques and also strategies for employing linear models in big data analytics. Hence, techniques of generalized linear models will be presented in the proposed book covering the needs of new learners, users of advanced techniques, researchers in need of statistical modelling of any data type and users of big data analytics wanting to increase predictive accuracy of classification and regression tree techniques. 284 pp. Englisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a wide range of topics to address the needs of several groups of users of rapidly growing methods of generalized linear models. Since the introduction of the idea of generalized linear models (GLM) in early seventies, during the past four decades the modelling of statistical data have experienced a major transformation from linear models based on normality assumption to a more flexible unified approach of generalized linear models.The number of readers and users of generalized linear models have increased manifold. In addition, the use of generalized linear models has expanded in many new fields of applications where statistical models are being employed at an increasing rate. It is important to note here that the learners and users of GLM have a widely varied background in different disciplines. Considering these pressing needs, this book focuses on: (i) upper undergraduate and graduate level students in need of a thorough understanding about the basic concepts of generalized linear models along with appropriate applications; (ii) researchers and users in need of advanced generalized linear models for analysing bivariate or multivariate data stemming from longitudinal or repeated measures data; and (iii) new challenges to analyse big data where the traditional techniques fail to provide any reasonable modelling strategy. In other words, this book starts with a thorough background of the generalized linear models for the new learners, then provides multivariate extensions to advanced level techniques for researchers and users in various disciplines, and finally some innovative modelling strategies are introduced using generalized linear models in the emerging field of big data analytics. It provides materials for new learners, for users/researchers who are in need of more advanced techniques and also strategies for employing linear models in big data analytics. Hence, techniques of generalized linear models will be presented in the proposed book covering the needs of new learners, users of advanced techniques, researchers in need of statistical modelling of any data type and users of big data analytics wanting to increase predictive accuracy of classification and regression tree techniques. 284 pp. Englisch.…