The aim of this graduate textbook is to provide a comprehensive advanced course in the theory of statistics covering those topics in estimation, testing, and large sample theory which a graduate student might typically need to learn as preparation for work on a Ph.D. An important strength of this book is that it provides a mathematically rigorous account of both classical and Bayesian inference in order to give readers a broad perspective. For example, the "uniformly most powerful" approach to testing is contrasted with available decision-theoretic approaches.
Commencing with chapters on probability models and the theory of sufficient statistics, the author covers decision theory, hypothesis testing, estimation, equivariance, large sample theory, hierarchical models, and, finally, sequential analysis. Every chapter concludes with exercises which range in difficulty from the easy to the challenging. As a result, this textbook provides an excellent course in modern theoretical statistics.
From the reviews:
"Another excellent book in theory of statistics is by Mark J. Schervish. ... Readers will enjoy reading this book to see how differently the theory can be presented ... . This well written book contains nine chapters and four appendices. ... Each chapter has both easy and challenging problems. The book is suitable for graduate level statistical theory courses. Examples and illustrations are well explained. I liked the author's presentation, and learned a lot from the book. I highly recommend this book to theoretical statisticians." (Ramalingam Shanmugam, Journal of Statistical Computation and Simulation, Vol. 74 (11), November, 2004)
Series: Springer Series in Statistics
Number Of Pages: 716
Published: 13th December 1996
Publisher: Springer-Verlag New York Inc.
Country of Publication: US
Dimensions (cm): 24.16 x 16.2
Weight (kg): 1.14
Edition Number: 2