# all of statistics a concise course in statistical inference springer texts in statistics

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## All Of Statistics

**Author :**Larry Wasserman

**ISBN :**9780387217369

**Genre :**Mathematics

**File Size :**72. 22 MB

**Format :**PDF, ePub

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Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con ducted in statistics departments while data mining and machine learning re search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo rithms are more scalable than statisticians ever thought possible. Formal sta tistical theory is more pervasive than computer scientists had realized.

## All Of Statistics

**Author :**Larry Wasserman

**ISBN :**0387402721

**Genre :**Computers

**File Size :**67. 2 MB

**Format :**PDF

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This book surveys a broad range of topics in probability and mathematical statistics. It provides the statistical background that a computer scientist needs to work in the area of machine learning.

## All Of Nonparametric Statistics

**Author :**Larry Wasserman

**ISBN :**0387306234

**Genre :**Mathematics

**File Size :**26. 79 MB

**Format :**PDF, ePub

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This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.

## A First Course In Bayesian Statistical Methods

**Author :**Peter D. Hoff

**ISBN :**9780387924076

**Genre :**Mathematics

**File Size :**25. 11 MB

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A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

## Statistical Inference

**Author :**Robert B. Ash

**ISBN :**9780486481586

**Genre :**Mathematics

**File Size :**27. 10 MB

**Format :**PDF, Kindle

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This book offers a brief course in statistical inference that requires only a basic familiarity with probability and matrix and linear algebra. Ninety problems with solutions make it an ideal choice for self-study as well as a helpful review of a wide-ranging topic with important uses to professionals in business, government, public administration, and other fields. 2011 edition.

## Theoretical Statistics

**Author :**Robert W. Keener

**ISBN :**0387938397

**Genre :**Mathematics

**File Size :**66. 9 MB

**Format :**PDF, ePub

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Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential. The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis. The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix.

## Weighing The Odds

**Author :**David Williams

**ISBN :**052100618X

**Genre :**Mathematics

**File Size :**34. 34 MB

**Format :**PDF, ePub, Docs

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Advanced textbook; many examples and exercises, often with hints or solutions; code provided for computational examples and simulations.