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.…mehr
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.
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.
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.
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