
Statistical Decision Theory
Estimation, Testing, and Selection
By: Friedrich Liese, Klaus-J Miescke
Hardcover | 9 July 2008
At a Glance
700 Pages
23.39 x 15.6 x 3.81
Hardcover
$329.00
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Industry Reviews
From the reviews:
"The central theme of this book is 'what optimal decisions are in general and in specific decision problems, and how to derive them'; optimality is understood in terms of the expected loss, i.e. the risk functional, or some function of it.... This monograph is, undoubtedly, a significant event in the development of statistical decision theory." (Yurij S. Kharin, American Mathematical Society, Mathematical Reviews on the Web, MR2421720)
"This book is unique in offering a fuller point of view of selection rules,.... An other feature is that is combines innovation and tradition. This monograph uniquely synthesizes otherwise disparate materials, while establishing connections between classical and modern decision theory. Actually it creates a bridge between the classical results of mathematical statistics and the modern asymptotic decision theory.... also provides a broad coverage of both the frequentist and the Bayes approaches in decision theory." (2009 European Mathematical Society, FIZ Karlsruhe & Springer-Velag, ZBl 1154.62008)
"The central theme of this book is the nature of optimal decisions, in both general and specific problems, and how to reach them. ... This is a deep theoretical and mathematical presentation of decision theory, which I imagine will become a leading reference work for researchers in the area." (David J. Hand, International Statistical Review, Vol. 76 (3), 2008)
"This monograph is written not only as a basis for graduate courses, but also as a reference tool. Readers should be familiar with basic concepts of probability theory, mathematical statistics, and analysis. ... This book also provides a broad coverage of both the frequentist and the Bayes approaches in decision theory. The Bayes approach is considered to be a useful decision-theoretic framework among others, and it is used heavily throughout the book ... ." (R. Schlittgen, Zentralblatt MATH, Vol. 1154, 2009)
"The authors characterize this book as a monograph intended for graduate students in mathematical Statistics. As such, it should be suitable as the basis for an advanced class in decision theory. ...The book's coverage is both comprehensive and general. ...a solid addition to the literature of decision theory from a formal mathematical statistics approach. ..." ((Journal of the American Statistical Association, September 2009, Vol. 104, No. 487)
| Preface | p. VI |
| Statistical Models | p. 1 |
| Exponential Families | p. 2 |
| Priors and Conjugate Priors for Exponential Families | p. 16 |
| Divergences in Binary Models | p. 31 |
| Information in Bayes Models | p. 52 |
| L[subscript 2]-Differentiability, Fisher Information | p. 58 |
| Solutions to Selected Problems | p. 67 |
| Tests in Models with Monotonicity Properties | p. 75 |
| Stochastic Ordering and Monotone Likelihood Ratio | p. 75 |
| Tests in Binary Models and Models with MLR | p. 83 |
| Solutions to Selected Problems | p. 100 |
| Statistical Decision Theory | p. 104 |
| Decisions in Statistical Models | p. 104 |
| Convergence of Decisions | p. 114 |
| Continuity Properties of the Risk | p. 118 |
| Minimum Average Risk, Bayes Risk, Posterior Risk | p. 121 |
| Bayes and Minimax Decisions | p. 133 |
| [Gamma]-Minimax Decisions | p. 141 |
| Minimax Theorem | p. 146 |
| Complete Classes | p. 149 |
| Solutions to Selected Problems | p. 153 |
| Comparison of Models, Reduction by Sufficiency | p. 156 |
| Comparison and Randomization of Models | p. 156 |
| Comparison of Finite Models by Standard Distributions | p. 166 |
| Sufficiency in Dominated Models | p. 177 |
| Completeness, Ancillarity, and Minimal Sufficiency | p. 188 |
| Solutions to Selected Problems | p. 194 |
| Invariant Statistical Decision Models | p. 198 |
| Invariant Models and Invariant Statistics | p. 198 |
| Invariant Decision Problems | p. 204 |
| Hunt-Stein Theorem | p. 213 |
| Equivariant Estimators, Girshick-Savage Theorem | p. 222 |
| Solutions to Selected Problems | p. 232 |
| Large Sample Approximations of Models and Decisions | p. 235 |
| Distances of Statistical Models | p. 235 |
| Convergence of Models | p. 241 |
| Weak Convergence of Binary Models | p. 248 |
| Asymptotically Normal Models | p. 265 |
| Gaussian Models | p. 266 |
| The LAN and ULAN Property | p. 269 |
| Asymptotic Lower Risk Bounds, Hajek-LeCam Bound | p. 281 |
| Solutions to Selected Problems | p. 287 |
| Estimation | p. 293 |
| Lower Information Bounds in Estimation Problems | p. 293 |
| Unbiased Estimators with Minimal Risk | p. 301 |
| Bayes and Generalized Bayes Estimators | p. 309 |
| Admissibility of Estimators, Shrinkage Estimators | p. 315 |
| Consistency of Estimators | p. 319 |
| Consistency of M-Estimators and MLEs | p. 319 |
| Consistency in Bayes Models | p. 347 |
| Asymptotic Distributions of Estimators | p. 359 |
| Asymptotic Distributions of M-Estimators | p. 359 |
| Asymptotic Distributions of MLEs | p. 374 |
| Asymptotic Normality of the Posterior | p. 379 |
| Local Asymptotic Optimality of MLEs | p. 386 |
| Solutions to Selected Problems | p. 400 |
| Testing | p. 406 |
| Best Tests for Exponential Families | p. 406 |
| Tests for One-Parameter Exponential Families | p. 406 |
| Tests in Multivariate Normal Distributions | p. 417 |
| Tests for d-Parameter Exponential Families | p. 420 |
| Confidence Regions and Confidence Bounds | p. 431 |
| Bayes Tests | p. 437 |
| Uniformly Best Invariant Tests | p. 443 |
| Exponential Rates of Error Probabilities | p. 450 |
| U-Statistics and Rank Statistics | p. 454 |
| Statistics with Estimated Parameters | p. 470 |
| Asymptotic Null Distribution | p. 473 |
| Locally Asymptotically Optimal Tests | p. 485 |
| Testing of Univariate Parameters | p. 485 |
| Testing of Multivariate Parameters | p. 503 |
| Solutions to Selected Problems | p. 510 |
| Selection | p. 516 |
| The Selection Models | p. 516 |
| Optimal Point Selections | p. 520 |
| Point Selections, Loss, and Risk | p. 520 |
| Point Selections in Balanced Models | p. 528 |
| Point Selections in Unbalanced Models | p. 536 |
| Point Selections with Estimation | p. 542 |
| Optimal Subset Selections | p. 547 |
| Subset Selections, Loss, and Risk | p. 547 |
| [Gamma]-Minimax Subset Selections | p. 556 |
| Optimal Multistage Selections | p. 561 |
| Common Sample Size per Stage and Hard Elimination | p. 561 |
| Bayes Sampling Designs for Adaptive Sampling | p. 582 |
| Asymptotically Optimal Point Selections | p. 587 |
| Exponential Rate of Error Probabilities | p. 587 |
| Locally Asymptotically Optimal Point Selections | p. 592 |
| Rank Selection Rules | p. 607 |
| Solutions to Selected Problems | p. 609 |
| Appendix: Topics from Analysis, Measure Theory, and Probability Theory | p. 615 |
| Topics from Analysis | p. 615 |
| Topics from Measure Theory | p. 617 |
| Topics from Probability Theory | p. 623 |
| Appendix: Common Notation and Distributions | p. 631 |
| Common Notation | p. 631 |
| Common Distributions | p. 635 |
| References | p. 640 |
| Author Index | p. 663 |
| Subject Index | p. 668 |
| Table of Contents provided by Ingram. All Rights Reserved. |
ISBN: 9780387731933
ISBN-10: 0387731938
Series: Springer Series in Statistics
Published: 9th July 2008
Format: Hardcover
Language: English
Number of Pages: 700
Audience: College, Tertiary and University
Publisher: Springer Nature B.V.
Country of Publication: GB
Dimensions (cm): 23.39 x 15.6 x 3.81
Weight (kg): 1.09
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