November 25, 2020

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Theory and Methods of Statistics

Theory and Methods of Statistics
Author : P.K. Bhattacharya,Prabir Burman
Publisher : Academic Press
Release Date : 2016-06-23
Category : Mathematics
Total pages :544
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Theory and Methods of Statistics covers essential topics for advanced graduate students and professional research statisticians. This comprehensive resource covers many important areas in one manageable volume, including core subjects such as probability theory, mathematical statistics, and linear models, and various special topics, including nonparametrics, curve estimation, multivariate analysis, time series, and resampling. The book presents subjects such as "maximum likelihood and sufficiency," and is written with an intuitive, heuristic approach to build reader comprehension. It also includes many probability inequalities that are not only useful in the context of this text, but also as a resource for investigating convergence of statistical procedures. Codifies foundational information in many core areas of statistics into a comprehensive and definitive resource Serves as an excellent text for select master’s and PhD programs, as well as a professional reference Integrates numerous examples to illustrate advanced concepts Includes many probability inequalities useful for investigating convergence of statistical procedures

Theory and Methods of Statistics

Theory and Methods of Statistics
Author : P. K. Bhattacharya,Prabir Burman
Publisher : Academic Press
Release Date : 2015-10-01
Category :
Total pages :630
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Theory and Methods of Statistics covers essential topics for advanced graduate students and professional research statisticians. This comprehensive resource covers many important areas in one manageable volume, including core subjects such as probability theory, mathematical statistics, and linear models, and various special topics, including nonparametrics, curve estimation, multivariate analysis, time series, and resampling. The book presents subjects such as Maximum Likelihood and Sufficiency, and is written with an intuitive, heuristic approach to build reader comprehension. It also includes many probability inequalities that are not only useful in the context of this text, but also as a resource for investigating convergence of statistical procedures. Codifies foundational information in many core areas of statistics into a comprehensive and definitive resource Appropriate for select master's program use as well as for PhD programs and professional reference Integrates numerous examples to illustrate advanced concepts Includes many probability inequalities useful for investigating convergence of statistical procedures.

Robust Statistics

Robust Statistics
Author : Ricardo A. Maronna,Douglas R. Martin,Victor J. Yohai
Publisher : Wiley
Release Date : 2006-05-12
Category : Mathematics
Total pages :436
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Classical statistical techniques fail to cope well with deviations from a standard distribution. Robust statistical methods take into account these deviations while estimating the parameters of parametric models, thus increasing the accuracy of the inference. Research into robust methods is flourishing, with new methods being developed and different applications considered. Robust Statistics sets out to explain the use of robust methods and their theoretical justification. It provides an up-to-date overview of the theory and practical application of the robust statistical methods in regression, multivariate analysis, generalized linear models and time series. This unique book: Enables the reader to select and use the most appropriate robust method for their particular statistical model. Features computational algorithms for the core methods. Covers regression methods for data mining applications. Includes examples with real data and applications using the S-Plus robust statistics library. Describes the theoretical and operational aspects of robust methods separately, so the reader can choose to focus on one or the other. Supported by a supplementary website featuring time-limited S-Plus download, along with datasets and S-Plus code to allow the reader to reproduce the examples given in the book. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is ideal for researchers, practitioners and graduate students of statistics, electrical, chemical and biochemical engineering, and computer vision. There is also much to benefit researchers from other sciences, such as biotechnology, who need to use robust statistical methods in their work.

Bayes Linear Statistics

Bayes Linear Statistics
Author : Michael Goldstein,David Wooff
Publisher : John Wiley & Sons
Release Date : 2007-04-30
Category : Mathematics
Total pages :536
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Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian methodology in that it establishes simpler approaches to belief specification and analysis based around expectation judgements. Bayes Linear Statistics presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field. The text provides a thorough coverage of Bayes linear analysis, from the development of the basic language to the collection of algebraic results needed for efficient implementation, with detailed practical examples. The book covers: The importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification. Simple ways to use partial prior specifications to adjust beliefs, given observations. Interpretative and diagnostic tools to display the implications of collections of belief statements, and to make stringent comparisons between expected and actual observations. General approaches to statistical modelling based upon partial exchangeability judgements. Bayes linear graphical models to represent and display partial belief specifications, organize computations, and display the results of analyses. Bayes Linear Statistics is essential reading for all statisticians concerned with the theory and practice of Bayesian methods. There is an accompanying website hosting free software and guides to the calculations within the book.

Statistics

Statistics
Author : Donald A. Berry,Bernard William Lindgren
Publisher : Duxbury Resource Center
Release Date : 1996
Category : Mathematics
Total pages :702
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1. Probability 2. Discrete Random Variables 3. Averages 4. Bernoulli and Related Variables 5. Continuous Random Variables 6. Families of Continuous Distributions 7. Organizing and Describing Data 8. Samples, Statistics, and Sampling Distributions 9. Estimation 10. Significance Testing 11. Tests as Decision Rules 12. Comparing Two Populations 13. Goodness of Fit 14. Analysis of Variance 15. Regression

Statistical Hypothesis Testing

Statistical Hypothesis Testing
Author : Ning-Zhong Shi,Jian Tao
Publisher : World Scientific Publishing Company
Release Date : 2008-09-29
Category : Mathematics
Total pages :320
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This book presents up-to-date theory and methods of statistical hypothesis testing based on measure theory. The so-called statistical space is a measurable space adding a family of probability measures. Most topics in the book will be developed based on this term. The book includes some typical data sets, such as the relation between race and the death penalty verdict, the behavior of food intake of two kinds of Zucker rats, and the per capita income and expenditure in China during the 1978–2002 period. Emphasis is given to the process of finding appropriate statistical techniques and methods of evaluating these techniques.

Criminology And Criminal Justice

Criminology And Criminal Justice
Author : Maddan,Walker
Publisher : Jones & Bartlett Publishers
Release Date : 2010-10-22
Category : Law
Total pages :120
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It is commonly recognized that the basis of science stems from the intersection of theory, research methods, and statistics. Criminology and Criminal Justice: Theory, Research Methods, and Statistics is designed to help readers understand the integrated relationship between these critical topics in the field of Criminal Justice. Each chapter pertains to a particular criminological theory, relevant qualitative and quantitative research methodologies, and various statistical techniques used to analyze data. This accessible text illustrates how theory, methods, and statistics play an active role in ensuing careers in law enforcement, corrections, law, and more.

Essential Statistical Inference

Essential Statistical Inference
Author : Dennis D. Boos,L A Stefanski
Publisher : Springer Science & Business Media
Release Date : 2013-02-06
Category : Mathematics
Total pages :568
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​This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems. An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology. Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods. ​

Learning from Data

Learning from Data
Author : Vladimir Cherkassky,Filip M. Mulier
Publisher : John Wiley & Sons
Release Date : 2007-09-10
Category : Computers
Total pages :560
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An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

Statistics

Statistics
Author : M. Afzal Beg,Mirza Miraj Din
Publisher : Unknown
Release Date : 1983
Category : Statistics
Total pages :129
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Robust Statistics

Robust Statistics
Author : Ricardo A. Maronna,R. Douglas Martin,Victor J. Yohai,Matías Salibián-Barrera
Publisher : Wiley
Release Date : 2018-12-03
Category : Mathematics
Total pages :464
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A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.

Sampling Theory and Methods

Sampling Theory and Methods
Author : S. Sampath
Publisher : Alpha Science Int'l Ltd.
Release Date : 2005
Category : Business & Economics
Total pages :199
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Sampling Theory and Methods presents in detail several sampling schemes like simple random sampling, unequal probability sampling methods, systematic, stratified, cluster and multistage sampling. In addition to sampling schemes a number of estimating methods which include ratio and regression estimators are also discussed. The use of superpopulation models is covered in detail along with recent developments including estimation of distribution functions, adaptive sampling schemes etc. New to the Second Edition: *Contents reorganized to establish a coherent link between various concepts *Several numerical examples associated with real life solutions for bringing out the relevance of theory in real life context

Nonparametric Statistics: Theory And Methods

Nonparametric Statistics: Theory And Methods
Author : Jayant V Deshpande,Uttara Naik-nimbalkar,Isha Dewan
Publisher : World Scientific
Release Date : 2017-10-17
Category : Mathematics
Total pages :280
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The number of books on Nonparametric Methodology is quite small as compared to, say, on Design of Experiments, Regression Analysis, Multivariate Analysis, etc. Because of being perceived as less effective, nonparametric methods are still the second choice. Actually, it has been demonstrated time and again that they are useful. We feel that there is still need for proper texts/applications/reference books on Nonparametric Methodology.This book will introduce various types of data encountered in practice and suggest the appropriate nonparametric methods, discuss their properties through null and non-null distributions whenever possible and demonstrate the very minor loss in power and efficiency in the nonparametric method, if any.The book will cover almost all topics of current interest such as bootstrapping, ranked set sampling, techniques for censored data and Bayesian analysis under nonparametric set ups.

Statistical Methods in Molecular Evolution

Statistical Methods in Molecular Evolution
Author : Rasmus Nielsen
Publisher : Springer Science & Business Media
Release Date : 2005-04-21
Category : Science
Total pages :505
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In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders of field and they will take the reader from basic introductory material to the state-of-the-art statistical methods. This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book. From the reviews: "...Overall this is a very useful book in an area of increasing importance." Journal of the Royal Statistical Society "I find Statistical Methods in Molecular Evolution very interesting and useful. It delves into problems that were considered very difficult just several years ago...the book is likely to stimulate the interest of statisticians that are unaware of this exciting field of applications. It is my hope that it will also help the 'wet lab' molecular evolutionist to better understand mathematical and statistical methods." Marek Kimmel for the Journal of the American Statistical Association, September 2006 "Who should read this book? We suggest that anyone who deals with molecular data (who does not?) and anyone who asks evolutionary questions (who should not?) ought to consult the relevant chapters in this book." Dan Graur and Dror Berel for Biometrics, September 2006 "Coalescence theory facilitates the merger of population genetics theory with phylogenetic approaches, but still, there are mostly two camps: phylogeneticists and population geneticists. Only a few people are moving freely between them. Rasmus Nielsen is certainly one of these researchers, and his work so far has merged many population genetic and phylogenetic aspects of biological research under the umbrella of molecular evolution. Although Nielsen did not contribute a chapter to his book, his work permeates all its chapters. This book gives an overview of his interests and current achievements in molecular evolution. In short, this book should be on your bookshelf." Peter Beerli for Evolution, 60(2), 2006

Mathematical Methods of Statistics

Mathematical Methods of Statistics
Author : Harald Cramér
Publisher : Princeton University Press
Release Date : 1999-04-12
Category : Mathematics
Total pages :575
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In this classic of statistical mathematical theory, Harald Cramér joins the two major lines of development in the field: while British and American statisticians were developing the science of statistical inference, French and Russian probabilitists transformed the classical calculus of probability into a rigorous and pure mathematical theory. The result of Cramér's work is a masterly exposition of the mathematical methods of modern statistics that set the standard that others have since sought to follow. For anyone with a working knowledge of undergraduate mathematics the book is self contained. The first part is an introduction to the fundamental concept of a distribution and of integration with respect to a distribution. The second part contains the general theory of random variables and probability distributions while the third is devoted to the theory of sampling, statistical estimation, and tests of significance.