From Finite Sample to Asymptotic Methods in Statistics PDF Download
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Author: Pranab K. Sen
Publisher: Cambridge University Press
ISBN: 0521877229
Category : Mathematics
Languages : en
Pages : 399
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Book Description
A broad view of exact statistical inference and the development of asymptotic statistical inference.
Author: Pranab K. Sen
Publisher: Cambridge University Press
ISBN: 0521877229
Category : Mathematics
Languages : en
Pages : 399
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Book Description
A broad view of exact statistical inference and the development of asymptotic statistical inference.
Author: John F. Monahan
Publisher: Cambridge University Press
ISBN: 1139498002
Category : Computers
Languages : en
Pages : 465
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Book Description
This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author's website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder–Mead search algorithm.
Author: Yuri Suhov
Publisher: Cambridge University Press
ISBN: 1316062201
Category : Mathematics
Languages : en
Pages : 477
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Book Description
Probability and statistics are as much about intuition and problem solving as they are about theorem proving. Consequently, students can find it very difficult to make a successful transition from lectures to examinations to practice because the problems involved can vary so much in nature. Since the subject is critical in so many applications from insurance to telecommunications to bioinformatics, the authors have collected more than 200 worked examples and examination questions with complete solutions to help students develop a deep understanding of the subject rather than a superficial knowledge of sophisticated theories. With amusing stories and historical asides sprinkled throughout, this enjoyable book will leave students better equipped to solve problems in practice and under exam conditions.
Author: A. W. van der Vaart
Publisher: Cambridge University Press
ISBN: 9780521784504
Category : Mathematics
Languages : en
Pages : 470
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Book Description
This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master's level statistics text, this book will also give researchers an overview of research in asymptotic statistics.
Author: Loïc Chaumont
Publisher: Cambridge University Press
ISBN: 1107606551
Category : Mathematics
Languages : en
Pages : 301
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Book Description
Over 100 exercises with detailed solutions, insightful notes and references for further reading. Ideal for beginning researchers.
Author: Yu. M. Suhov
Publisher: Cambridge University Press
ISBN: 1107603587
Category : Mathematics
Languages : en
Pages : 477
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Book Description
A valuable resource for students and teachers alike, this second edition contains more than 200 worked examples and exam questions.
Author: Thomas S. Ferguson
Publisher: Routledge
ISBN: 1351470051
Category : Mathematics
Languages : en
Pages : 192
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Book Description
A Course in Large Sample Theory is presented in four parts. The first treats basic probabilistic notions, the second features the basic statistical tools for expanding the theory, the third contains special topics as applications of the general theory, and the fourth covers more standard statistical topics. Nearly all topics are covered in their multivariate setting.The book is intended as a first year graduate course in large sample theory for statisticians. It has been used by graduate students in statistics, biostatistics, mathematics, and related fields. Throughout the book there are many examples and exercises with solutions. It is an ideal text for self study.
Author: Evarist Giné
Publisher: Cambridge University Press
ISBN: 1009022784
Category : Mathematics
Languages : en
Pages : 706
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Book Description
In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.
Author: David J. Olive
Publisher: Springer
ISBN: 3319049720
Category : Mathematics
Languages : en
Pages : 438
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Book Description
This text is for a one semester graduate course in statistical theory and covers minimal and complete sufficient statistics, maximum likelihood estimators, method of moments, bias and mean square error, uniform minimum variance estimators and the Cramer-Rao lower bound, an introduction to large sample theory, likelihood ratio tests and uniformly most powerful tests and the Neyman Pearson Lemma. A major goal of this text is to make these topics much more accessible to students by using the theory of exponential families. Exponential families, indicator functions and the support of the distribution are used throughout the text to simplify the theory. More than 50 ``brand name" distributions are used to illustrate the theory with many examples of exponential families, maximum likelihood estimators and uniformly minimum variance unbiased estimators. There are many homework problems with over 30 pages of solutions.
Author: Pierre Moulin
Publisher: Cambridge University Press
ISBN: 1107185920
Category : Mathematics
Languages : en
Pages : 423
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Book Description
A mathematically accessible textbook introducing all the tools needed to address modern inference problems in engineering and data science.
