## The Elements of Statistical Learning

*Data Mining, Inference, and Prediction*

**Author**: Trevor Hastie,Robert Tibshirani,Jerome Friedman

**Publisher:**Springer Science & Business Media

**ISBN:**0387216065

**Category:**Mathematics

**Page:**536

**View:**8124

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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

## Elements of Statistics

**Author**: Fergus Daly

**Publisher:**Financial Times/Prentice Hall

**ISBN:**9780201422788

**Category:**Mathematical statistics

**Page:**682

**View:**7072

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Elements of Statistics provides an introduction to statistics and probability for students across a wide range of disciplines. The emphasis on problem solving through analysis of data is enhanced by extensive use of real data sets throughout, drawn from a wide range of subject areas to highlight the diversity of statistics. Written to support self-study, this book provides an excellent foundation in statistics.

## Elements of Statistical Mechanics

**Author**: D. ter Haar

**Publisher:**Elsevier

**ISBN:**008053080X

**Category:**Psychology

**Page:**416

**View:**3954

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Following the Boltzmann-Gibbs approach to statistical mechanics, this new edition of Dr ter Haar's important textbook, Elements of Statistical Mechanics, provides undergraduates and more senior academics with a thorough introduction to the subject. Each chapter is followed by a problem section and detailed bibliography. The first six chapters of the book provide a thorough introduction to the basic methods of statistical mechanics and indeed the first four may be used as an introductory course in themselves. The last three chapters offer more detail on the equation of state, with special emphasis on the van der Waals gas; the second-quantisation approach to many-body systems, with an examination of two-time temperature-dependent Green functions; phase transitions, including various approximation methods for treating the Ising model, a brief discussion of the exact solution of the two-dimensional square Ising model, and short introductions to renormalisation group methods and the Yang and Lee theory of phase transitions. In the problem section which follows each chapter the reader is asked to complete proofs of basic theory and to apply that theory to various physical situations. Each chapter bibliography includes papers which are of historical interest. A further help to the reader are the solutions to selected problems which appear at the end of the book.

## Elements of Statistical Computing

*NUMERICAL COMPUTATION*

**Author**: R.A. Thisted

**Publisher:**Routledge

**ISBN:**1351452746

**Category:**Mathematics

**Page:**448

**View:**5257

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Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science. Elements of Statistical Computing covers the broad usage of computing in statistics. It provides a comprehensive account of the most important computational statistics. Included are discussions of numerical analysis, numerical integration, and smoothing. The author give special attention to floating point standards and numerical analysis; iterative methods for both linear and nonlinear equation, such as Gauss-Seidel method and successive over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter, projection-pursuit methods, density estimation, and other computer-intensive techniques.

## An Introduction to Statistical Learning

*with Applications in R*

**Author**: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani

**Publisher:**Springer Science & Business Media

**ISBN:**1461471389

**Category:**Mathematics

**Page:**426

**View:**1563

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

## The Elements of Statistics

*With Applications to Economics and the Social Sciences*

**Author**: James Bernard Ramsey,H. Joseph Newton,Jane L. Harvill

**Publisher:**South-Western Pub

**ISBN:**9780534371111

**Category:**Business & Economics

**Page:**648

**View:**4373

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Designed for instructors who want to stress the understanding of basic concepts and the development of "statistical intuition," this book demonstrates that statistical reasoning is everywhere and that statistical concepts are as important to students' personal lives as they are to their future professional careers. Ramsey aims to develop statistically literacy - from the ability to read and think critically about statistics published in popular media to the ability to analyze and act upon statistics gathered in the business world. The underlying philosophy of this book is that given a reasonable level of depth in the analysis, the student can later acquire a much more extensive, and even more intensive, exposure to statistics on their own or in the context of the work environment. Some use of calculus is included. Use of the computer is integrated throughout.

## Elements of Probability and Statistics

**Author**: Baisnab A P,A P Baisnab Manoranjan Jas

**Publisher:**Tata McGraw-Hill Education

**ISBN:**9780074600412

**Category:**Mathematical statistics

**Page:**408

**View:**3292

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## Schaum's Outline of Elements of Statistics I: Descriptive Statistics and Probability

**Author**: Stephen Bernstein,Ruth Bernstein

**Publisher:**McGraw Hill Professional

**ISBN:**9780070050235

**Category:**Mathematics

**Page:**354

**View:**3860

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Schaum's Outlines give you the information your teachers expect you to know in a handy and succinct format - without overwhelming you with unnecessary detail.

## the elements of statistics and dynamics

**Author**: N.A

**Publisher:**CUP Archive

**ISBN:**N.A

**Category:**

**Page:**N.A

**View:**3406

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## Elements of statistics

*an introduction to probability and statistical inference*

**Author**: Donald R. Byrkit

**Publisher:**Van Nostrand Reinhold Company

**ISBN:**N.A

**Category:**Mathematics

**Page:**482

**View:**7490

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Organization and presentation of data; Measures of location and dispersion; Probability; Probability distributions; The binomial distribution; The normal distribution; Estimation of parameters; Hypothesis testing; The chi-square distribution; Analysis of variance; Correlation and regression; Nonparametric tests; Mathematical review.

## The Theory of Probability and the Elements of Statistics

**Author**: Boris Vladimirovich Gnedenko

**Publisher:**American Mathematical Soc.

**ISBN:**9780821837467

**Category:**Science

**Page:**529

**View:**3429

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This classic book is intended to be the first introduction to probability and statistics written with an emphasis on the analytic approach to the problems discussed. Topics include the axiomatic setup of probability theory, polynomial distribution, finite Markov chains, distribution functions and convolution, the laws of large numbers (weak and strong), characteristic functions, the central limit theorem, infinitely divisible distributions, and Markov processes. Written in a clear and concise style, this book by Gnedenko can serve as a textbook for undergraduate and graduate courses in probability.

## The Elements of Statistics

**Author**: Richard K. Gaumnitz,Merton P. Stoltz.

**Publisher:**N.A

**ISBN:**N.A

**Category:**Statistics

**Page:**230

**View:**8109

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## Elements of Statistical Thermodynamics

*Second Edition*

**Author**: Leonard K. Nash

**Publisher:**Courier Corporation

**ISBN:**0486137465

**Category:**Science

**Page:**144

**View:**4671

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Encompassing essentially all aspects of statistical mechanics that appear in undergraduate texts, this concise, elementary treatment shows how an atomic-molecular perspective yields new insights into macroscopic thermodynamics. 1974 edition.

## Elements of Statistics for the Life and Social Sciences

**Author**: Braxton M. Alfred

**Publisher:**Springer Science & Business Media

**ISBN:**1461247446

**Category:**Mathematics

**Page:**190

**View:**539

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This book was written to myself at about the time I began graduate studies in anthropology-the sort of thing a Samuel Beckett character might do. It is about the conduct of research. In a very real sense the purpose is partially to compensate for the inadequacies of my professors. Perhaps this is what education is about. The effort has not been an unqualified success, but it has been extremely gratifying. I was trained in anthropology. After completing the Ph. D. I went to Stanford on a post-doctoral fellowship. At the time, this was a novelty and the depart ment was not prepared for such a thing. To stay occupied I began attending lectures, seminars, and discussion groups in mathematics and statistics. This was about the luckiest choice I ever made. The excitement was easily as intense as that which I experienced upon encountering anthropology. On one oc casion I innocently and independently proved a theorem that had first been done 2000 years earlier. It is currently used as an exercise in high school mathematics so it is neither difficult nor arcane. Learning all this did not tarnish my sense of discovery. (On reflection I am puzzled by my failure to have seen all this "beauty" when I was exposed to it as an undergraduate. The unparalleled excellence of the Stanford program was undoubtedly responsible for my belated conversion.

## Elements of Statistics

**Author**: Sir Arthur Lyon Bowley

**Publisher:**London : P.S. King & son, Limited

**ISBN:**N.A

**Category:**Statistics

**Page:**330

**View:**5638

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## Schaum's Outline of Elements of Statistics II: Inferential Statistics

**Author**: Stephen Bernstein,Ruth Bernstein

**Publisher:**McGraw Hill Professional

**ISBN:**9780071346375

**Category:**Mathematics

**Page:**451

**View:**5117

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Schaum's Outlines give you the information your teachers expect you to know in a handy and succinct format - without overwhelming you with unnecessary detail.

## Elements of Statistics with Application to Economic Dat

**Author**: Harold Thayer Davis,William Franklin Cram Nelson

**Publisher:**N.A

**ISBN:**9781258399290

**Category:**

**Page:**448

**View:**8765

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## Elements of Statistical Disclosure Control

**Author**: Leon Willenborg,Ton de Waal

**Publisher:**Springer Science & Business Media

**ISBN:**1461301211

**Category:**Business & Economics

**Page:**261

**View:**6222

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Statistical disclosure control is the discipline that deals with producing statistical data that are safe enough to be released to external researchers. This book concentrates on the methodology of the area. It deals with both microdata (individual data) and tabular (aggregated) data. The book attempts to develop the theory from what can be called the paradigm of statistical confidentiality: to modify unsafe data in such a way that safe (enough) data emerge, with minimum information loss. This book discusses what safe data, are, how information loss can be measured, and how to modify the data in a (near) optimal way. Once it has been decided how to measure safety and information loss, the production of safe data from unsafe data is often a matter of solving an optimization problem. Several such problems are discussed in the book, and most of them turn out to be hard problems that can be solved only approximately. The authors present new results that have not been published before. The book is not a description of an area that is closed, but, on the contrary, one that still has many spots awaiting to be more fully explored. Some of these are indicated in the book. The book will be useful for official, social and medical statisticians and others who are involved in releasing personal or business data for statistical use. Operations researchers may be interested in the optimization problems involved, particularly for the challenges they present. Leon Willenborg has worked at the Department of Statistical Methods at Statistics Netherlands since 1983, first as a researcher and since 1989 as a senior researcher. Since 1989 his main field of research and consultancy has been statistical disclosure control. From 1996-1998 he was the project coordinator of the EU co-funded SDC project.

## Elements of statistics for market research

**Author**: Pierre Weber

**Publisher:**N.A

**ISBN:**N.A

**Category:**Business & Economics

**Page:**110

**View:**1457

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