This volume contains six early mathematical works, four papers on fiducial inference, five on transformations, and twenty-seven on a miscellany of topics in mathematical statistics. Several previously unpublished works are included.
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The book is well organized...the crisp style of presentation distinguishes the book from most commonly used texts...I recommend it highly as a reference handbook for use by biology students...Also, it will serve as a convenient source for statistical practitioners. The book has the broadest coverage of relevant topics among applied texts at this level. I have already found it highly useful on my own shelf. The authors are obviously well acquainted with their business and communicate their ideas clearly and succinctly.
-Journal of the American Statistical Society
-Journal of the American Statistical Society
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29 of 29 people found the following review helpful:
5.0 out of 5 stars
nice collection of counterexamples,
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This review is from: Counterexamples in Probability And Statistics (Wadsworth and Brooks/Cole Statistics/Probability Series) (Hardcover)
Joe Romano is a Stanford Professor known for his work on resampling. Andy Siegel was a contemporary of mine in graduate school at the Stanford University Statistics Department. Although the title suggests statistical examples, the text is primarily a collection of counterexamples in probability. The authors write in an engaging style. This was the first book for both authors but each has gone on to author or co-author at least one other book.
This book is perfect for a graduate student in mathematics or statistics with a serious interest in probability. It is particularly good if they have just completed their first advanced course in probability theory as the concepts would be fresh. A warning though. This is great for those seriously interested in probability but would be difficult reading for those with just a passing interest.
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Inside This Book
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First Sentence:
The fundamental notion underlying the theory of probability and statistics is that of a "probability space." Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
risk equivariant estimator, uniformly most powerful invariant test, uniformly smallest risk, uniformly minimum variance unbiased estimator, uniformly most powerful unbiased test, unique maximum likelihood estimator, least favorable distribution, pth mean, risk unbiased, most powerful unbiased tests, discrete probability function, nonrandomized tests, unique unbiased estimator, monotone likelihood ratio, uniformly most powerful test, sample from the uniform distribution, squared error loss function, nonmeasurable set, partial averages, minimax estimator, maximal invariant, invariant tests, most powerful level, common probability space, median unbiased Key Phrases - Capitalized Phrases (CAPs): (learn more)
Borel-Cantelli Lemma, Neyman-Pearson Lemma, Cantor Set, Lehmann-Scheffe Theorem, Information Inequality, Three Series Theorem, Kolmogorov's Zero-One Law New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The fundamental notion underlying the theory of probability and statistics is that of a "probability space." Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
risk equivariant estimator, uniformly most powerful invariant test, uniformly smallest risk, uniformly minimum variance unbiased estimator, uniformly most powerful unbiased test, unique maximum likelihood estimator, least favorable distribution, pth mean, risk unbiased, most powerful unbiased tests, discrete probability function, nonrandomized tests, unique unbiased estimator, monotone likelihood ratio, uniformly most powerful test, sample from the uniform distribution, squared error loss function, nonmeasurable set, partial averages, minimax estimator, maximal invariant, invariant tests, most powerful level, common probability space, median unbiased Key Phrases - Capitalized Phrases (CAPs): (learn more)
Borel-Cantelli Lemma, Neyman-Pearson Lemma, Cantor Set, Lehmann-Scheffe Theorem, Information Inequality, Three Series Theorem, Kolmogorov's Zero-One Law New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 1
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16
books
cite this book:
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- Mathematical Statistics: Basic Ideas and Selected Topics, Vol I (2nd Edition) by Peter J. Bickel in Back Matter
- Essential Mathematics for Political and Social Research (Analytical Methods for Social Research) by Jeff Gill on page 378, and Back Matter
- Sojourns And Extremes of Stochastic Processes (Wadsworth Biology Series) by Simeon Berman in Front Matter
- Simulation Methodology for Statisticians, Operations Analysts, and Engineers. Vol. 1 by P. W. A. Lewis in Front Matter
- The Collected Works of John W. Tukey: Philosophy and Principles of Data Analysis 1965-1986, Volume IV (Tukey, John Wilder//Collected Works of John W Tukey) by L.V. Jones in Front Matter
- Fundamental Probability: A Computational Approach by Marc Paolella in Back Matter
See all 16 books citing this book
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