Anirban DasGupta – författare

This book developed out of my year-long course on asymptotic theory at Purdue University. Numeroustopics covered in this book are available in the literature in a scattered manner, and they are brought together under one umbrella in this book.

​​​This book is a unique compendium of core topics in the Statistics Ph.D. program, as well as covering the latest statistical theory, graduate probability and simulation techniques. The Complete Guide to Statistical Theory, Simulation and Probability: A Bridge to the Future gives an elaborate treatment of standard statistics topics such as parametric inference, basic theory of linear models, large sample theory, as well as more advanced topics such as robust estimation, density estimation, bootstrap, multiple testing, and the latest breakthrough developments such as the LASSO and thresholding and regularization. It also gives self-contained treatments of standard graduate probability and major Monte Carlo techniques, including MCMC. As such, this book can be used as an all-purpose text in the statistics Ph.D. programs, as well as a unique research reference.The book provides 918 exercises, and an additional 98 exercises at the end of the book. The book also has a complete and comprehensive synopsis of real analysis, calculus, linear algebra and matrix theory as an invaluable source of consultation for students, instructors, and researchers.

The book covers all of the standard topics in basic probability, such as combinatorial probability, discrete and continuous distributions, moment generating functions, fundamental probability inequalities, the central limit theorem, and joint and conditional distributions of discrete and continuous random variables.

This book contains a little more than 20 of Debabrata Basu's  most significant articles and writings. Debabrata Basu is internationally  known for his highly influential and fundamental contributions  to the foundations of statistics, survey sampling, sufficiency,  and invariance. The major theorem bearing his name has had numerous  applications to statistics and probability. The articles in this volume  are reprints of the original articles, in a chronological order. The  book also contains eleven commentaries written by some of the most  distinguished scholars in the area of foundations and statistical  inference. These commentaries are by George Casella and V. Gopal,  Phil Dawid, Tom DiCiccio and Alastair Young, Malay Ghosh, Jay kadane,  Glen Meeden, Robert Serfling, Jayaram Sethuraman, Terry Speed, and  Alan Welsh.

This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning.

Probability theory is one branch of mathematics that is simultaneously deep and immediately applicable in diverse areas of human endeavor. It is as fundamental as calculus. Calculus explains the external world, and probability theory helps predict a lot of it. In addition, problems in probability theory have an innate appeal, and the answers are often structured and strikingly beautiful. A solid background in probability theory and probability models will become increasingly more useful in the twenty-?rst century, as dif?cult new problems emerge, that will require more sophisticated models and analysis. Thisisa text onthe fundamentalsof thetheoryofprobabilityat anundergraduate or ?rst-year graduate level for students in science, engineering,and economics. The only mathematical background required is knowledge of univariate and multiva- ate calculus and basic linear algebra. The book covers all of the standard topics in basic probability, such as combinatorial probability, discrete and continuous distributions, moment generating functions, fundamental probability inequalities, the central limit theorem, and joint and conditional distributions of discrete and continuous random variables. But it also has some unique features and a forwa- looking feel.

This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance.This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.

This book developed out of my year-long course on asymptotic theory at Purdue University. Numeroustopics covered in this book are available in the literature in a scattered manner, and they are brought together under one umbrella in this book.

This book contains a little more than 20 of Debabrata Basu's most significant articles and writings. Debabrata Basu is internationally known for his highly influential and fundamental contributions to the foundations of statistics, survey sampling, sufficiency, and invariance.

The mission, relevance and intellectual orientation of development studies is increasingly challenged from various fronts such as decoloniality, ‘global development’ and randomized control trials. The essays featured in this collection together argue for the need of the field to reclaim its critical political economy tradition. Building on the contributions of Ashwani Saith, the contributions touch upon many of the central questions of development studies centred around structural change, labour and inequality.