From the reviews: "This is definitely not your typical book on theory. The approach that Dasgupta has taken with this far-reaching volume is to explore important results and applications of asymptotic theory without emphasizing the intricate mathematical details. The focus is on the forest rather than on the trees, and this results in a readable text that, for the most part, should be accessible to anyone with a first-year graduate-level course in statistical theory. I would imagine that this book woult be very useful as a first place to look for help in solving many problems in asymptotics. The book can provide an overview of the key issues, some ideas, and a path to more detail." (Biometrics, September 2008) "Presents an encyclopedic treatment of classic as well as contemporary large sample theory, including both statistical problems and probabilistic issues and tools." (Journal of Economic Literature, Volume 46, no. 3, 2008) "... a nice handbook and reference material. This is a different book on the asymptotic theory and its use in probability and statistical inference. It covers a wide range of divergent topics where the large sample theory is useful and can be naturally applied. ... The book is will organized and clearly written. The book works well as a reference text for a theoretical statistician working with the asymptotics. It can also be used as a textbook for several topics of the graduate courses." (International Statistical Review,2009, 77, 1) "This book provides a comprehensive overview of asymptotic theory in probability and mathematical statistics. ... The text is written in a very clear style ... . the book is a very good choice as a first reading. It also contains a large collection of inequalities from linear algebra, probability and analysis that are of importance in mathematical statistics. This collection makes the volume even more valuable as a reference for students as well as research workers." (Zentralblatt MATH, Vol. 1154, 2009) "This amazing book covers an enormous variety of topics from modern statistics and probability...Among the great achievements of the author is not only the panoramic coverage of modern stochastics but also in demonstrating convincingly the fundamental role of probability theory in any kind of statistical inference problems...An unreplaceable source of information for anyone studying probability and statistics...An invaluable reference to be acquired by any good science library." (Journal of the Royal Statistical Society) "Contains a total of 35 (!) chapters, covering both theoretical foundations and many ... applications of asymptotic statistics. The chapters can broadly be classified in two categories: more classic large sample theory chapters, and chapters containing topics which are usually not treated in other books on asymptotics. ... I would recommend this book to readers who have attended courses on probability theory ... and mathematical statistics. ... Someone who searches a good and exhaustive reference book for asymptotic statistics ... will certainly appreciate this book."--- (Bjorn Bornkamp, Statistical Papers, Vol. 51, 2010) "This book provides a very broad coverage of both classical and contemporary topics, with an emphasis on the conceptual discussion of results, issues, tools and implications. ... This makes the book quite different from other books on asymptotics and provides an invaluable reference for anyone studying probability and statistics. It can be used to design graduate-level courses with various emphases, to assign for independent reading, and to have a comprehensive overview of asymptotic theory." (Wenbo V. Li, Mathematical Reviews, Issue 2011 m)

• Basic Convergence Concepts and Theorems.- Metrics, Information Theory, Convergence, and Poisson Approximations.- More General Weak and Strong Laws and the Delta Theorem.- Transformations.- More General Central Limit Theorems.- Moment Convergence and Uniform Integrability.- Sample Percentiles and Order Statistics.- Sample Extremes.- Central Limit Theorems for Dependent Sequences.- Central Limit Theorem for Markov Chains.- Accuracy of Central Limit Theorems.- Invariance Principles.- Edgeworth Expansions and Cumulants.- Saddlepoint Approximations.- U-statistics.- Maximum Likelihood Estimates.- M Estimates.- The Trimmed Mean.- Multivariate Location Parameter and Multivariate Medians.- Bayes Procedures and Posterior Distributions.- Testing Problems.- Asymptotic Efficiency in Testing.- Some General Large-Deviation Results.- Classical Nonparametrics.- Two-Sample Problems.- Goodness of Fit.- Chi-square Tests for Goodness of Fit.- Goodness of Fit with Estimated Parameters.- The Bootstrap.- Jackknife.- Permutation Tests.- Density Estimation.- Mixture Models and Nonparametric Deconvolution.- High-Dimensional Inference and False Discovery.- A Collection of Inequalities in Probability, Linear Algebra, and Analysis.

From the reviews: "This is definitely not your typical book on theory. The approach that Dasgupta has taken with this far-reaching volume is to explore important results and applications of asymptotic theory without emphasizing the intricate mathematical details. The focus is on the forest rather than on the trees, and this results in a readable text that, for the most part, should be accessible to anyone with a first-year graduate-level course in statistical theory. I would imagine that this book woult be very useful as a first place to look for help in solving many problems in asymptotics. The book can provide an overview of the key issues, some ideas, and a path to more detail." (Biometrics, September 2008) "Presents an encyclopedic treatment of classic as well as contemporary large sample theory, including both statistical problems and probabilistic issues and tools." (Journal of Economic Literature, Volume 46, no. 3, 2008) "... a nice handbook and reference material. This is a different book on the asymptotic theory and its use in probability and statistical inference. It covers a wide range of divergent topics where the large sample theory is useful and can be naturally applied. ... The book is will organized and clearly written. The book works well as a reference text for a theoretical statistician working with the asymptotics. It can also be used as a textbook for several topics of the graduate courses." (International Statistical Review,2009, 77, 1) "This book provides a comprehensive overview of asymptotic theory in probability and mathematical statistics. ... The text is written in a very clear style ... . the book is a very good choice as a first reading. It also contains a large collection of inequalities from linear algebra, probability and analysis that are of importance in mathematical statistics. This collection makes the volume even more valuable as a reference for students as well as research workers." (Zentralblatt MATH, Vol. 1154, 2009) "This amazing book covers an enormous variety of topics from modern statistics and probability...Among the great achievements of the author is not only the panoramic coverage of modern stochastics but also in demonstrating convincingly the fundamental role of probability theory in any kind of statistical inference problems...An unreplaceable source of information for anyone studying probability and statistics...An invaluable reference to be acquired by any good science library." (Journal of the Royal Statistical Society) "Contains a total of 35 (!) chapters, covering both theoretical foundations and many ... applications of asymptotic statistics. The chapters can broadly be classified in two categories: more classic large sample theory chapters, and chapters containing topics which are usually not treated in other books on asymptotics. ... I would recommend this book to readers who have attended courses on probability theory ... and mathematical statistics. ... Someone who searches a good and exhaustive reference book for asymptotic statistics ... will certainly appreciate this book."--- (Bjorn Bornkamp, Statistical Papers, Vol. 51, 2010) "This book provides a very broad coverage of both classical and contemporary topics, with an emphasis on the conceptual discussion of results, issues, tools and implications. ... This makes the book quite different from other books on asymptotics and provides an invaluable reference for anyone studying probability and statistics. It can be used to design graduate-level courses with various emphases, to assign for independent reading, and to have a comprehensive overview of asymptotic theory." (Wenbo V. Li, Mathematical Reviews, Issue 2011 m)

• Basic Convergence Concepts and Theorems.- Metrics, Information Theory, Convergence, and Poisson Approximations.- More General Weak and Strong Laws and the Delta Theorem.- Transformations.- More General Central Limit Theorems.- Moment Convergence and Uniform Integrability.- Sample Percentiles and Order Statistics.- Sample Extremes.- Central Limit Theorems for Dependent Sequences.- Central Limit Theorem for Markov Chains.- Accuracy of Central Limit Theorems.- Invariance Principles.- Edgeworth Expansions and Cumulants.- Saddlepoint Approximations.- U-statistics.- Maximum Likelihood Estimates.- M Estimates.- The Trimmed Mean.- Multivariate Location Parameter and Multivariate Medians.- Bayes Procedures and Posterior Distributions.- Testing Problems.- Asymptotic Efficiency in Testing.- Some General Large-Deviation Results.- Classical Nonparametrics.- Two-Sample Problems.- Goodness of Fit.- Chi-square Tests for Goodness of Fit.- Goodness of Fit with Estimated Parameters.- The Bootstrap.- Jackknife.- Permutation Tests.- Density Estimation.- Mixture Models and Nonparametric Deconvolution.- High-Dimensional Inference and False Discovery.- A Collection of Inequalities in Probability, Linear Algebra, and Analysis.

From the reviews: "This is definitely not your typical book on theory. The approach that Dasgupta has taken with this far-reaching volume is to explore important results and applications of asymptotic theory without emphasizing the intricate mathematical details. The focus is on the forest rather than on the trees, and this results in a readable text that, for the most part, should be accessible to anyone with a first-year graduate-level course in statistical theory. I would imagine that this book woult be very useful as a first place to look for help in solving many problems in asymptotics. The book can provide an overview of the key issues, some ideas, and a path to more detail." (Biometrics, September 2008) "Presents an encyclopedic treatment of classic as well as contemporary large sample theory, including both statistical problems and probabilistic issues and tools." (Journal of Economic Literature, Volume 46, no. 3, 2008) "... a nice handbook and reference material. This is a different book on the asymptotic theory and its use in probability and statistical inference. It covers a wide range of divergent topics where the large sample theory is useful and can be naturally applied. ... The book is will organized and clearly written. The book works well as a reference text for a theoretical statistician working with the asymptotics. It can also be used as a textbook for several topics of the graduate courses." (International Statistical Review,2009, 77, 1) "This book provides a comprehensive overview of asymptotic theory in probability and mathematical statistics. ... The text is written in a very clear style ... . the book is a very good choice as a first reading. It also contains a large collection of inequalities from linear algebra, probability and analysis that are of importance in mathematical statistics. This collection makes the volume even more valuable as a reference for students as well as research workers." (Zentralblatt MATH, Vol. 1154, 2009) "This amazing book covers an enormous variety of topics from modern statistics and probability...Among the great achievements of the author is not only the panoramic coverage of modern stochastics but also in demonstrating convincingly the fundamental role of probability theory in any kind of statistical inference problems...An unreplaceable source of information for anyone studying probability and statistics...An invaluable reference to be acquired by any good science library." (Journal of the Royal Statistical Society) "Contains a total of 35 (!) chapters, covering both theoretical foundations and many ... applications of asymptotic statistics. The chapters can broadly be classified in two categories: more classic large sample theory chapters, and chapters containing topics which are usually not treated in other books on asymptotics. ... I would recommend this book to readers who have attended courses on probability theory ... and mathematical statistics. ... Someone who searches a good and exhaustive reference book for asymptotic statistics ... will certainly appreciate this book."--- (Bjorn Bornkamp, Statistical Papers, Vol. 51, 2010) "This book provides a very broad coverage of both classical and contemporary topics, with an emphasis on the conceptual discussion of results, issues, tools and implications. ... This makes the book quite different from other books on asymptotics and provides an invaluable reference for anyone studying probability and statistics. It can be used to design graduate-level courses with various emphases, to assign for independent reading, and to have a comprehensive overview of asymptotic theory." (Wenbo V. Li, Mathematical Reviews, Issue 2011 m)

• Basic Convergence Concepts and Theorems.- Metrics, Information Theory, Convergence, and Poisson Approximations.- More General Weak and Strong Laws and the Delta Theorem.- Transformations.- More General Central Limit Theorems.- Moment Convergence and Uniform Integrability.- Sample Percentiles and Order Statistics.- Sample Extremes.- Central Limit Theorems for Dependent Sequences.- Central Limit Theorem for Markov Chains.- Accuracy of Central Limit Theorems.- Invariance Principles.- Edgeworth Expansions and Cumulants.- Saddlepoint Approximations.- U-statistics.- Maximum Likelihood Estimates.- M Estimates.- The Trimmed Mean.- Multivariate Location Parameter and Multivariate Medians.- Bayes Procedures and Posterior Distributions.- Testing Problems.- Asymptotic Efficiency in Testing.- Some General Large-Deviation Results.- Classical Nonparametrics.- Two-Sample Problems.- Goodness of Fit.- Chi-square Tests for Goodness of Fit.- Goodness of Fit with Estimated Parameters.- The Bootstrap.- Jackknife.- Permutation Tests.- Density Estimation.- Mixture Models and Nonparametric Deconvolution.- High-Dimensional Inference and False Discovery.- A Collection of Inequalities in Probability, Linear Algebra, and Analysis.