From Finite Sample To Asymptotic Methods In Statistics
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From Finite Sample To Asymptotic Methods In Statistics
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Author : Pranab Kumar Sen
language : en
Publisher:
Release Date : 2014-05-14
From Finite Sample To Asymptotic Methods In Statistics written by Pranab Kumar Sen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-14 with Mathematics categories.
A broad view of exact statistical inference and the development of asymptotic statistical inference.
From Finite Sample To Asymptotic Methods In Statistics
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Author : Pranab K. Sen
language : en
Publisher: Cambridge University Press
Release Date : 2010
From Finite Sample To Asymptotic Methods In Statistics written by Pranab K. Sen and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Mathematics categories.
A broad view of exact statistical inference and the development of asymptotic statistical inference.
A Course In Large Sample Theory
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Author : Thomas S. Ferguson
language : en
Publisher: Routledge
Release Date : 2017-09-06
A Course In Large Sample Theory written by Thomas S. Ferguson and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-06 with Mathematics categories.
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.
Numerical Methods Of Statistics
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Author : John F. Monahan
language : en
Publisher: Cambridge University Press
Release Date : 2011-04-18
Numerical Methods Of Statistics written by John F. Monahan and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-18 with Computers categories.
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.
Applied Asymptotics
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Author : A. R. Brazzale
language : en
Publisher: Cambridge University Press
Release Date : 2007-05-31
Applied Asymptotics written by A. R. Brazzale and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-31 with Mathematics categories.
In fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods.
Probability And Statistics By Example
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Author : Yu. M. Suhov
language : en
Publisher: Cambridge University Press
Release Date : 2014-09-22
Probability And Statistics By Example written by Yu. M. Suhov and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-22 with Mathematics categories.
A valuable resource for students and teachers alike, this second edition contains more than 200 worked examples and exam questions.
Exercises In Probability
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Author : Loïc Chaumont
language : en
Publisher: Cambridge University Press
Release Date : 2012-07-19
Exercises In Probability written by Loïc Chaumont and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-19 with Mathematics categories.
Over 100 exercises with detailed solutions, insightful notes and references for further reading. Ideal for beginning researchers.
Statistical Principles For The Design Of Experiments
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Author : R. Mead
language : en
Publisher: Cambridge University Press
Release Date : 2012-09-13
Statistical Principles For The Design Of Experiments written by R. Mead and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-13 with Mathematics categories.
Focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis in various disciplines.
Statistical Theory And Inference
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Author : David J. Olive
language : en
Publisher: Springer
Release Date : 2014-05-07
Statistical Theory And Inference written by David J. Olive and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-07 with Mathematics categories.
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.
A Course In Statistics With R
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Author : Prabhanjan N. Tattar
language : en
Publisher: John Wiley & Sons
Release Date : 2016-03-15
A Course In Statistics With R written by Prabhanjan N. Tattar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-15 with Computers categories.
Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets