The theory of dispersion models straddles both statistics and probability, and involves an encyclopedic collection of tools, such as exponential families, asymptotic theory, stochastic processes, Tauber theory, infinite divisibility, and stable distributions. The Theory of Dispersion Models introduces the reader to these models, which serve as error distributions for generalized linear models, and looks at their applications within this context.
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First Sentence:
The theory of errors was developed by Gauss primarily in relation to the needs of astronomers and surveyors, making rather accurate angular measurements. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
unit deviance, unit variance function, exponential dispersion models, renormalized saddlepoint approximation, hyperbolic secant distribution, infinitely divisible type, canonical parameter domain, hyperbola distribution, mean value mapping, simplex distribution, natural exponential family, scale transformation property, reproductive version, quadratic variance functions, natural exponential families, cumulant function, reproductive cases, central limit type, convex support, cumulant generating function, saddlepoint approximations, crude residuals, convolution formula, deviance residual, reproductive form Key Phrases - Capitalized Phrases (CAPs): (learn more)
Exercises Exercise, Some Tweedie New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The theory of errors was developed by Gauss primarily in relation to the needs of astronomers and surveyors, making rather accurate angular measurements. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
unit deviance, unit variance function, exponential dispersion models, renormalized saddlepoint approximation, hyperbolic secant distribution, infinitely divisible type, canonical parameter domain, hyperbola distribution, mean value mapping, simplex distribution, natural exponential family, scale transformation property, reproductive version, quadratic variance functions, natural exponential families, cumulant function, reproductive cases, central limit type, convex support, cumulant generating function, saddlepoint approximations, crude residuals, convolution formula, deviance residual, reproductive form Key Phrases - Capitalized Phrases (CAPs): (learn more)
Exercises Exercise, Some Tweedie New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 66
books:
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- Encyclopedia of Statistical Sciences by Campbell B. Read in Back Matter (1), Back Matter (2), and Back Matter (3)
- Inference and Asymptotics (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by D.R. Cox in Front Matter, and Back Matter
- The Analysis of Contingency Tables, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Brian S. Everitt in Front Matter (1), and Front Matter (2)
- Asymptotic TECHNIQUES FOR USE IN STATISTICS (Monographs on Statistics and Applied Probability 31) by O.E. Barndorff-Nielsen in Front Matter, and Back Matter
- The Principles of Mathematical Analysis (International Series in Pure & Applied Mathematics) by Walter Rudin in Back Matter
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