by Sara van de Geer. Also, we did not include material due to David Donoho, lain Johnstone, and their school. We found our selves unprepared to write a distillate of the material. We did touch briefly on "nonparametrics," but not on "semiparamet rics." This is because we feel that the semiparametric situation has not yet been properly structured. We hope that the reader will find this book interesting and challenging, in spite of its shortcomings. The material was typed in LaTeX form by the authors them selves, borrowing liberally from the 1990 script by Chris Bush. It was reviewed anonymously by distinguished colleagues. We thank them for their kind encouragement. Very special thanks are due to Professor David Pollard who took time out of a busy schedule to give us a long list of suggestions. We did not follow them all, but we at least made attempts. We wish also to thank the staff of Springer-Verlag for their help, in particular editor John Kimmel, who tried to make us work with all deliberate speed. Thanks are due to Paul Smith, Te-Ching Chen and Ju-Yi-Yen, who helped with the last-minute editorial corrections.
"synopsis" may belong to another edition of this title.
From the reviews:
SHORT BOOK REVIEWS
"It is a very valuable book giving a coherent view of the basic concept and tools of the asymptotic theory in statistical inference."
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
"...short and mathematically very sophisticated. Its approach is modern and undoubtedly profound."
This volume is the second edition of a work that presents a coherent introduction to the subject of asymptotic statistics as it has developed in the past 50 years. The second edition differs from the first in that it has been made more 'reader friendly'. It also includes a new chapter, Chapter 4, on Gaussian and Poisson experiments because of their growing role in the field, especially in nonparametrics and semi-parametrics. Most of the subsequent chapters have been entirely rewritten and the nonparametrics of Chapter 7 have been ampliefied. Much of the material has been taught in a second year graduate course at Berkeley for 30 years. It represents a link between traditional material including maximum likelihood, and Wald's Theory of Statistical Decision Functions together with comparison and distances for experiments. This volume is not intended to replace monographs on specialized subjects, but it will help to place them in a coherent perspective.Lucien Le Cam is Professor of Statistics and Mathematics (Emeritus) at the University of California, Berkeley.
He is 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."About this title" may belong to another edition of this title.
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