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Añadir al carritoPaperback or Softback. Condición: New. Exponential Families in Theory and Practice. Book.
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Añadir al carritoPaperback. Condición: New. During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book connects exponential family theory with its applications in a way that doesn't require advanced mathematical preparation.
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Añadir al carritoKartoniert / Broschiert. Condición: New. Über den AutorBradley Efron is Professor Emeritus of Statistics and Biomedical Data Science at Stanford University. He is the inventor of the bootstrap method for assessing statistical accuracy. He has published extensively on stati.
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ISBN 10: 1108715664 ISBN 13: 9781108715669
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - This book is aimed at Ph.D. and advanced M.S. students of statistics who need to understand modern statistical theory both in its exponential family structure and its applications, without requiring advanced mathematical preparation. Connections with logistic regression, survival analysis, Bayesian methods, and false discovery rates are emphasized.
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Añadir al carritoPaperback. Condición: New. During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book connects exponential family theory with its applications in a way that doesn't require advanced mathematical preparation.
Idioma: Inglés
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ISBN 10: 1108715664 ISBN 13: 9781108715669
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Añadir al carritoTaschenbuch. Condición: Neu. Exponential Families in Theory and Practice | Bradley Efron | Taschenbuch | Institute of Mathematical Statistics Textbooks | Kartoniert / Broschiert | Englisch | 2022 | Cambridge University Press | EAN 9781108715669 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2022
ISBN 10: 1108715664 ISBN 13: 9781108715669
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Añadir al carritoPaperback. Condición: new. Paperback. During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book connects exponential family theory with its applications in a way that doesn't require advanced mathematical preparation. This book is aimed at Ph.D. and advanced M.S. students of statistics who need to understand modern statistical theory both in its exponential family structure and its applications, without requiring advanced mathematical preparation. Connections with logistic regression, survival analysis, Bayesian methods, and false discovery rates are emphasized. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108715664 ISBN 13: 9781108715669
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
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Publicado por Cambridge University Press, Cambridge, 2022
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Añadir al carritoPaperback. Condición: new. Paperback. During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book connects exponential family theory with its applications in a way that doesn't require advanced mathematical preparation. This book is aimed at Ph.D. and advanced M.S. students of statistics who need to understand modern statistical theory both in its exponential family structure and its applications, without requiring advanced mathematical preparation. Connections with logistic regression, survival analysis, Bayesian methods, and false discovery rates are emphasized. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2022
ISBN 10: 1108715664 ISBN 13: 9781108715669
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Añadir al carritoPaperback. Condición: new. Paperback. During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book connects exponential family theory with its applications in a way that doesn't require advanced mathematical preparation. This book is aimed at Ph.D. and advanced M.S. students of statistics who need to understand modern statistical theory both in its exponential family structure and its applications, without requiring advanced mathematical preparation. Connections with logistic regression, survival analysis, Bayesian methods, and false discovery rates are emphasized. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.