Sequential Methods and Their Applications

Interactively Run Simulations and Experiment with Real or Simulated Data to Make Sequential Analysis Come Alive Taking an accessible, nonmathematical approach to this field, Sequential Methods and Their Applications illustrates the efficiency of sequential methodologies when dealing with contemporary statistical challenges in many areas. The book first explores fixed sample size, sequential probability ratio, and nonparametric tests. It then presents numerous multistage estimation methods for fixed-width confidence interval as well as minimum and bounded risk problems. The book also describes multistage fixed-size confidence region methodologies, selection methodologies, and Bayesian estimation. Through diverse applications, each chapter provides valuable approaches for performing statistical experiments and facilitating real data analysis. Functional in a variety of statistical problems, the authors' interactive computer programs show how the methodologies discussed can be implemented in data analysis. Each chapter offers examples of input, output, and their interpretations. Available online, the programs provide the option to save some parts of an output so readers can revisit computer-generated data for further examination with exploratory data analysis. Through this book and its computer programs, readers will better understand the methods of sequential analysis and be able to use them in real-world settings.

Science & Math

Mukhopadhyay, Nitis

de Silva, Basil M.

Mathematics

Chapman and Hall/CRC

This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques. Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of distributions, and standard probability inequalities. It develops the Helmert transformation for normal distributions, introduces the notions of convergence, and spotlights the central limit theorems. Coverage highlights sampling distributions, Basu's theorem, Rao-Blackwellization and the Cramér-Rao inequality. The text also provides in-depth coverage of Lehmann-Scheffé theorems, focuses on tests of hypotheses, describes Bayesian methods and the Bayes' estimator, and develops large-sample inference. The author provides a historical context for statistics and statistical discoveries and answers to a majority of the end-of-chapter exercises. Designed primarily for a one-semester, first-year graduate course in probability and statistical inference, this text serves readers from varied backgrounds, ranging from engineering, economics, agriculture, and bioscience to finance, financial mathematics, operations and information management, and psychology.

Introductory Statistical Inference (Statistics: Textbooks and Monographs)

Probability and Statistical Inference (Statistics: A Series of Textbooks and Monographs)

Product Description 
Priced very competitively compared with other textbooks at this level! 
This gracefully organized textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts.\nBeginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics, Probability and Statistical Inference\nstudies the Helmert transformation for normal distributions and the waiting time between failures for exponential distributions 
develops notions of convergence in probability and distribution 
spotlights the central limit theorem (CLT) for the sample variance 
introduces sampling distributions and the Cornish-Fisher expansions 
concentrates on the fundamentals of sufficiency, information, completeness, and ancillarity 
explains Basu's Theorem as well as location, scale, and location-scale families of distributions 
covers moment estimators, maximum likelihood estimators (MLE), Rao-Blackwellization, and the Cramér-Rao inequality 
discusses uniformly minimum variance unbiased estimators (UMVUE) and Lehmann-Scheffé Theorems 
focuses on the Neyman-Pearson theory of most powerful (MP) and uniformly most powerful (UMP) tests of hypotheses, as well as confidence intervals 
includes the likelihood ratio (LR) tests for the mean, variance, and correlation coefficient 
summarizes Bayesian methods 
describes the monotone likelihood ratio (MLR) property 
handles variance stabilizing transformations 
provides a historical context for statistics and statistical discoveries 
showcases great statisticians through biographical notes Employing over 1400 equations to reinforce its subject matter, Probability and Statistical Inference is a groundbreaking text for first-year graduate and upper-level undergraduate courses in probability and statistical inference who have completed a calculus prerequisite, as well as a supplemental text for classes in Advanced Statistical Inference or Decision Theory. 
 About the Author 
Nitis Mukhopadhyay

CRC-Press

The only comprehensive guide to the theory and practice of one oftoday's most important probabilistic techniques The past 15 years have witnessed many significant advances insequential estimation, especially in the areas of three-stage andnonparametric methodology. Yet, until now, there were no referencesdevoted exclusively to this rapidly growing statisticalfield. Sequential Estimation is the first, single-source guide to thetheory and practice of both classical and modern sequentialestimation techniques--including parametric and nonparametricmethods. Researchers in sequential analysis will appreciate theunified, logically integrated treatment of the subject, as well ascoverage of important contemporary procedures not covered in moregeneral sequential analysis texts, such as: * Shrinkage estimation * Empirical and hierarchical Bayes procedures * Multistage sampling and accelerated sampling procedures * Time-sequential estimation * Sequential estimation in finite population sampling * Reliability estimation and capture-recapture methodologiesleading to sequential tagging schemes An indispensable resource for researchers in sequential analysis,Sequential Estimation is an ideal graduate-level text as well.

Sequential Estimation

Ghosh, Malay

Sen, Pranab Kumar

Wiley-Interscience

Priced very competitively compared with other textbooks at this level! This gracefully organized textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts. Beginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics, Probability and Statistical Inference 
<li>studies the Helmert transformation for normal distributions and the waiting time between failures for exponential distributions 
</li>
<li>develops notions of convergence in probability and distribution 
</li>
<li>spotlights the central limit theorem (CLT) for the sample variance 
</li>
<li>introduces sampling distributions and the Cornish-Fisher expansions 
</li>
<li>concentrates on the fundamentals of sufficiency, information, completeness, and ancillarity 
</li>
<li>explains Basu's Theorem as well as location, scale, and location-scale families of distributions 
</li>
<li>covers moment estimators, maximum likelihood estimators (MLE), Rao-Blackwellization, and the Cramér-Rao inequality 
</li>
<li>discusses uniformly minimum variance unbiased estimators (UMVUE) and Lehmann-Scheffé Theorems 
</li>
<li>focuses on the Neyman-Pearson theory of most powerful (MP) and uniformly most powerful (UMP) tests of hypotheses, as well as confidence intervals 
</li>
<li>includes the likelihood ratio (LR) tests for the mean, variance, and correlation coefficient 
</li>
<li>summarizes Bayesian methods 
</li>
<li>describes the monotone likelihood ratio (MLR) property 
</li>
<li>handles variance stabilizing transformations 
</li>
<li>provides a historical context for statistics and statistical discoveries 
</li>
<li>showcases great statisticians through biographical notes Employing over 1400 equations to reinforce its subject matter, Probability and Statistical Inference is a groundbreaking text for first-year graduate and upper-level undergraduate courses in probability and statistical inference who have completed a calculus prerequisite, as well as a supplemental text for classes in Advanced Statistical Inference or Decision Theory.</li>

"This useful volume provides a thorough synthesis of second-order asymptotics in multistage sampling methodologies for selection and ranking unifying available second-order results in general and applying them to a host of situations Contains, in each chapter, helpful Notes and Overviews to facilitate comprehension, as well as Complements and Problems for more in-depth study of specific topics!"

Multistage Selection and Ranking Procedures: Second Order Asymptotics (Statistics: A Series of Textbooks and Monographs)

Solanky, Tumulesh K.S.

This text presents the rigorous theory of probability and statistical inference using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Beginning with the basic ideas and techniques of probability theory and progressing to more rigorous topics, the author covers all of the topics typically addressed in a two-semester graduate or upper-level undergraduate course in probability and statistical inference, including hypothesis testing, Bayesian analysis, and sample-size determination. He reinforces important ideas and special techniques with drills and boxed summaries.

Probability and Statistical Inference, Second Edition (Statistics: A Series of Textbooks and Monographs)

Introductory Statistical Inference (STATISTICS, TEXTBOOKS AND MONOGRAPHS, 187)

Interactively Run Simulations and Experiment with Real or Simulated Data to Make Sequential Analysis Come Alive
 Taking an accessible, nonmathematical approach to this field, Sequential Methods and Their Applications illustrates the efficiency of sequential methodologies when dealing with contemporary statistical challenges in many areas.
 The book first explores fixed sample size, sequential probability ratio, and nonparametric tests. It then presents numerous multistage estimation methods for fixed-width confidence interval as well as minimum and bounded risk problems. The book also describes multistage fixed-size confidence region methodologies, selection methodologies, and Bayesian estimation. Through diverse applications, each chapter provides valuable approaches for performing statistical experiments and facilitating real data analysis.
 Functional in a variety of statistical problems, the authors’ interactive computer programs show how the methodologies discussed can be implemented in data analysis. Each chapter offers examples of input, output, and their interpretations. Available online, the programs provide the option to save some parts of an output so readers can revisit computer-generated data for further examination with exploratory data analysis.
 Through this book and its computer programs, readers will better understand the methods of sequential analysis and be able to use them in real-world settings.

See the best price to sell, buy, or rent books by Mukhopadhyay, Nitis. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Mukhopadhyay, Nitis

See the best price to sell, buy, or rent books by Mukhopadhyay, Nitis. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Sequential Methods and Their Applications

Introductory Statistical Inference (Statistics: Textbooks and Monographs)

Probability and Statistical Inference (Statistics: A Series of Textbooks and Monographs)

Sequential Estimation

Probability and Statistical Inference (Statistics: A Series of Textbooks and Monographs)

Multistage Selection and Ranking Procedures: Second Order Asymptotics (Statistics: A Series of Textbooks and Monographs)

Probability and Statistical Inference, Second Edition (Statistics: A Series of Textbooks and Monographs)

Introductory Statistical Inference (STATISTICS, TEXTBOOKS AND MONOGRAPHS, 187)

Sequential Methods and Their Applications