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Theory and Methods of Statistics

Theory and Methods of Statistics
A Book

by P.K. Bhattacharya,Prabir Burman

  • Publisher : Academic Press
  • Release : 2016-06-23
  • Pages : 544
  • ISBN : 0128041234
  • Language : En, Es, Fr & De
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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

Statistics

Statistics
Theory and Methods

by Donald A. Berry,Bernard William Lindgren

  • Publisher : Duxbury Resource Center
  • Release : 1996
  • Pages : 702
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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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

Bayes Linear Statistics

Bayes Linear Statistics
Theory and Methods

by Michael Goldstein,David Wooff

  • Publisher : John Wiley & Sons
  • Release : 2007-04-30
  • Pages : 536
  • ISBN : 9780470065679
  • Language : En, Es, Fr & De
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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.

Robust Statistics

Robust Statistics
Theory and Methods

by Ricardo A. Maronna,Douglas R. Martin,Victor J. Yohai

  • Publisher : Wiley
  • Release : 2006-05-12
  • Pages : 436
  • ISBN : 9780470010921
  • Language : En, Es, Fr & De
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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.

Statistical Hypothesis Testing

Statistical Hypothesis Testing
Theory and Methods

by Ning-Zhong Shi,Jian Tao

  • Publisher : World Scientific
  • Release : 2008
  • Pages : 307
  • ISBN : 9812814361
  • Language : En, Es, Fr & De
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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.

Nonparametric Statistics: Theory And Methods

Nonparametric Statistics: Theory And Methods
A Book

by Jayant V Deshpande,Uttara Naik-nimbalkar,Isha Dewan

  • Publisher : World Scientific
  • Release : 2017-10-17
  • Pages : 280
  • ISBN : 981466359X
  • Language : En, Es, Fr & De
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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.

Criminology And Criminal Justice

Criminology And Criminal Justice
Theory, Research Methods, And Statistics

by Maddan,Walker

  • Publisher : Jones & Bartlett Publishers
  • Release : 2010-10-22
  • Pages : 120
  • ISBN : 0763771392
  • Language : En, Es, Fr & De
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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
Theory and Methods

by Dennis D. Boos,L A Stefanski

  • Publisher : Springer Science & Business Media
  • Release : 2013-02-06
  • Pages : 568
  • ISBN : 1461448182
  • Language : En, Es, Fr & De
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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. ​

Statistics

Statistics
Theory and Methods

by M. Afzal Beg,Mirza Miraj Din

  • Publisher : Unknown Publisher
  • Release : 1983
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Learning from Data

Learning from Data
Concepts, Theory, and Methods

by Vladimir Cherkassky,Filip M. Mulier

  • Publisher : John Wiley & Sons
  • Release : 2007-09-10
  • Pages : 560
  • ISBN : 9780470140512
  • Language : En, Es, Fr & De
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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.

Sampling Theory and Methods

Sampling Theory and Methods
A Book

by S. Sampath

  • Publisher : CRC Press
  • Release : 2001
  • Pages : 184
  • ISBN : 9780849309809
  • Language : En, Es, Fr & De
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"The book 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 several estimating methods which include ratio and regression estimators are also discussed. The use of superpopulation models is also covered in detail. Some recent developments which include estimation of distribution functions, adaptive sampling schemes etc. are also presented."--BOOK JACKET.

Mathematical Methods of Statistics

Mathematical Methods of Statistics
A Book

by Harald Cramér

  • Publisher : Princeton University Press
  • Release : 1999-04-12
  • Pages : 575
  • ISBN : 9780691005478
  • Language : En, Es, Fr & De
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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.

Statistical Methods for Organizational Research

Statistical Methods for Organizational Research
Theory and Practice

by Chris Dewberry

  • Publisher : Psychology Press
  • Release : 2004
  • Pages : 340
  • ISBN : 041533425X
  • Language : En, Es, Fr & De
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'Statistical Methods for Organizational Research' provides a theoretical and practical introduction to the subject for students, researchers and practitioners involved in quantitative research.

Statistical Methods in Molecular Evolution

Statistical Methods in Molecular Evolution
A Book

by Rasmus Nielsen

  • Publisher : Springer Science & Business Media
  • Release : 2005-04-21
  • Pages : 505
  • ISBN : 9780387223339
  • Language : En, Es, Fr & De
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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

Algebraic Methods in Statistical Mechanics and Quantum Field Theory

Algebraic Methods in Statistical Mechanics and Quantum Field Theory
A Book

by Gérard G. Emch

  • Publisher : John Wiley & Sons
  • Release : 1972
  • Pages : 333
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Asymptotic Theory for Bootstrap Methods in Statistics

Asymptotic Theory for Bootstrap Methods in Statistics
A Book

by Rudolf J. Beran,Gilles R. Ducharme,Université de Montréal. Centre de recherches mathématiques

  • Publisher : Centre De Recherches Mathematiques
  • Release : 1991
  • Pages : 81
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Statistical Methods in Experimental Physics

Statistical Methods in Experimental Physics
A Book

by Frederick James

  • Publisher : World Scientific
  • Release : 2006
  • Pages : 345
  • ISBN : 981256795X
  • Language : En, Es, Fr & De
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The first edition of this classic book has become the authoritative reference for physicists desiring to master the finer points of statistical data analysis. This second edition contains all the important material of the first, much of it unavailable from any other sources. In addition, many chapters have been updated with considerable new material, especially in areas concerning the theory and practice of confidence intervals, including the important Feldman-Cousins method. Both frequentist and Bayesian methodologies are presented, with a strong emphasis on techniques useful to physicists and other scientists in the interpretation of experimental data and comparison with scientific theories. This is a valuable textbook for advanced graduate students in the physical sciences as well as a reference for active researchers.

An Introduction to Bayesian Analysis

An Introduction to Bayesian Analysis
Theory and Methods

by Jayanta K. Ghosh,Mohan Delampady,Tapas Samanta

  • Publisher : Springer Science & Business Media
  • Release : 2007-07-03
  • Pages : 354
  • ISBN : 0387354336
  • Language : En, Es, Fr & De
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This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior.

Applied Statistics

Applied Statistics
Theory and Problem Solutions with R

by Dieter Rasch,Rob Verdooren,Jürgen Pilz

  • Publisher : John Wiley & Sons
  • Release : 2019-10-07
  • Pages : 472
  • ISBN : 1119551528
  • Language : En, Es, Fr & De
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Instructs readers on how to use methods of statistics and experimental design with R software Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds from a strong theoretical background, but it is practically oriented to develop one's ability to tackle new and non-standard problems confidently. Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods of statistics and experimental design without going deep into the theory. Applied Statistics: Theory and Problem Solutions with R includes chapters that cover R package sampling procedures, analysis of variance, point estimation, and more. It follows on the heels of Rasch and Schott's Mathematical Statistics via that book's theoretical background—taking the lessons learned from there to another level with this book’s addition of instructions on how to employ the methods using R. But there are two important chapters not mentioned in the theoretical back ground as Generalised Linear Models and Spatial Statistics. Offers a practical over theoretical approach to the subject of applied statistics Provides a pre-experimental as well as post-experimental approach to applied statistics Features classroom tested material Applicable to a wide range of people working in experimental design and all empirical sciences Includes 300 different procedures with R and examples with R-programs for the analysis and for determining minimal experimental sizes Applied Statistics: Theory and Problem Solutions with R will appeal to experimenters, statisticians, mathematicians, and all scientists using statistical procedures in the natural sciences, medicine, and psychology amongst others.

Differential-Geometrical Methods in Statistics

Differential-Geometrical Methods in Statistics
A Book

by Shun-ichi Amari

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 294
  • ISBN : 1461250560
  • Language : En, Es, Fr & De
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From the reviews: "In this Lecture Note volume the author describes his differential-geometric approach to parametrical statistical problems summarizing the results he had published in a series of papers in the last five years. The author provides a geometric framework for a special class of test and estimation procedures for curved exponential families. ... ... The material and ideas presented in this volume are important and it is recommended to everybody interested in the connection between statistics and geometry ..." #Metrika#1 "More than hundred references are given showing the growing interest in differential geometry with respect to statistics. The book can only strongly be recommended to a geodesist since it offers many new insights into statistics on a familiar ground." #Manuscripta Geodaetica#2