Asymptotics in Statistics: Some Basic Concepts (Springer Series in Statistics)

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9780387950365: Asymptotics in Statistics: Some Basic Concepts (Springer Series in Statistics)

This is the second edition of a coherent introduction to the subject of asymptotic statistics as it has developed over the past 50 years. It differs from the first edition in that it is now more 'reader friendly' and also includes a new chapter on Gaussian and Poisson experiments, reflecting their growing role in the field. Most of the subsequent chapters have been entirely rewritten and the nonparametrics of Chapter 7 have been amplified. The volume is not intended to replace monographs on specialized subjects, but will help to place them in a coherent perspective. It thus represents a link between traditional material - such as maximum likelihood, and Wald's Theory of Statistical Decision Functions -- together with comparison and distances for experiments. Much of the material has been taught in a second year graduate course at Berkeley for 30 years.

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From the Publisher:

This book presents a coherent introduction to the subject of asymptotic statistics as it has developed in the past 50 years. Professor Le Cam is one of the major researchers in this area and this book will make his results accessible to graduate students and researchers in statistics.

About the Author:

Lucien Le Cam is recently deceased. He was Professor of Statistics and Mathematics (Emeritus) at the University of California, Berkeley and the author of numerous papers on asymptotics and Asymptotic Methods in Statistical Decision Theory, Springer Verlag (1986). He was co-editor, with J. Neyman and E. Scott of the Berkeley Symposia on Mathematical Statistics and Probability. Grace Lo Yang is Professor, Department of Mathematics, University of Maryland, College Park. She is a long time holder of a Faculty Appointment at the National Institute of Standards and Technology, Gaithersburg, MD. Her research activities include stochastic modeling in physical sciences and theory of incomplete data.

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