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

**Download Book All Of Statistics A Concise Course In Statistical Inference Springer Texts In Statistics in PDF format. You can Read Online All Of Statistics A Concise Course In Statistical Inference Springer Texts In Statistics here in PDF, EPUB, Mobi or Docx formats.**

## All Of Statistics

**Author :**Larry Wasserman

**ISBN :**9780387217369

**Genre :**Mathematics

**File Size :**47. 99 MB

**Format :**PDF, ePub, Mobi

**Download :**700

**Read :**780

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 non-parametric curve estimation, bootstrapping, and classification, 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 analysing data.

## All Of Statistics

**Author :**Larry Wasserman

**ISBN :**0387402721

**Genre :**Computers

**File Size :**88. 72 MB

**Format :**PDF

**Download :**588

**Read :**585

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 Statistics

**Author :**Larry Wasserman

**ISBN :**1468495526

**Genre :**

**File Size :**61. 55 MB

**Format :**PDF, ePub, Mobi

**Download :**301

**Read :**487

## All Of Nonparametric Statistics

**Author :**Larry Wasserman

**ISBN :**0387306234

**Genre :**Mathematics

**File Size :**39. 21 MB

**Format :**PDF, ePub, Mobi

**Download :**786

**Read :**979

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.

## Theoretical Statistics

**Author :**Robert W. Keener

**ISBN :**0387938397

**Genre :**Mathematics

**File Size :**31. 43 MB

**Format :**PDF, ePub

**Download :**240

**Read :**178

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.

## Statistical Theory

**Author :**Felix Abramovich

**ISBN :**9781439851845

**Genre :**Mathematics

**File Size :**74. 13 MB

**Format :**PDF, ePub, Docs

**Download :**930

**Read :**231

Designed for a one-semester advanced undergraduate or graduate course, Statistical Theory: A Concise Introduction clearly explains the underlying ideas and principles of major statistical concepts, including parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, Bayesian inference, and elements of decision theory. It introduces these topics on a clear intuitive level using illustrative examples in addition to the formal definitions, theorems, and proofs. Based on the authors’ lecture notes, this student-oriented, self-contained book maintains a proper balance between the clarity and rigor of exposition. In a few cases, the authors present a "sketched" version of a proof, explaining its main ideas rather than giving detailed technical mathematical and probabilistic arguments. Chapters and sections marked by asterisks contain more advanced topics and may be omitted. A special chapter on linear models shows how the main theoretical concepts can be applied to the well-known and frequently used statistical tool of linear regression. Requiring no heavy calculus, simple questions throughout the text help students check their understanding of the material. Each chapter also includes a set of exercises that range in level of difficulty.

## An Introduction To Statistical Inference And Its Applications With R

**Author :**Michael W. Trosset

**ISBN :**1584889489

**Genre :**Mathematics

**File Size :**61. 50 MB

**Format :**PDF, ePub, Docs

**Download :**724

**Read :**888

Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples—not to perform entire analyses. After discussing the importance of chance in experimentation, the text develops basic tools of probability. The plug-in principle then provides a transition from populations to samples, motivating a variety of summary statistics and diagnostic techniques. The heart of the text is a careful exposition of point estimation, hypothesis testing, and confidence intervals. The author then explains procedures for 1- and 2-sample location problems, analysis of variance, goodness-of-fit, and correlation and regression. He concludes by discussing the role of simulation in modern statistical inference. Focusing on the assumptions that underlie popular statistical methods, this textbook explains how and why these methods are used to analyze experimental data.