Alan M Polansky

Introduction to Statistical Limit Theory

• Gebundenes Buch

Produktbeschreibung

"Those moving on to advanced statistics typically lack the mathematical foundation that allows them to make full use of statistical limit theory. This accessible resource reviews approximation theory and limit theory for sequences of functions and basic notions of functional analysis. It provides detailed arguments that show how underlying mathematical and statistical theory work together. Among its unique qualities, the text covers expansion theory, which is becoming increasingly important in modern applications. It also discusses bootstrap, kernel smoothing, and Markov chain Monte Carlo and includes a wide array of examples and problems from the fundamental to very advanced"--Provided by publisher.

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Helping readers develop a good understanding of asymptotic theory, this text provides a thorough yet accessible treatment of common modes of convergence and their related tools used in statistics. The author explains as much of the background material as possible and incorporates detailed proofs and explanations of the results. The text includes many end-of-chapter exercises and experiments that range in level of difficulty from easy to advanced. Numerous examples illustrate the application of asymptotic theory to modern statistical problems. A solutions manual is available upon qualified course adoption.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 646
• Erscheinungstermin: 13. Januar 2011
• Englisch
• Abmessung: 234mm x 156mm x 35mm
• Gewicht: 1075g
• ISBN-13: 9781420076608
• ISBN-10: 1420076604
• Artikelnr.: 29946526

Autorenporträt

Alan M. Polansky is an associate professor in the Division of Statistics at Northern Illinois University. Dr. Polansky is the author of Observed Confidence Levels: Theory and Application (CRC Press, October 2007). His research interests encompass nonparametric statistics and industrial applications of statistics.

Inhaltsangabe

Sequences of Real Numbers and Functions. Random Variables and
Characteristic Functions. Convergence of Random Variables. Convergence of
Distributions. Convergence of Moments. Central Limit Theorems. Asymptotic
Expansions for Distributions. Asymptotic Expansions for Random Variables.
Differentiable Statistical Functionals. Parametric Inference. Nonparametric
Inference. Appendices. References.