By Larry Wasserman

ISBN-10: 0387217363

ISBN-13: 9780387217369

ISBN-10: 1441923225

ISBN-13: 9781441923226

This booklet is for those that are looking to examine chance and records fast. It brings jointly a number of the major principles in glossy records in a single position. The booklet is acceptable for college students and researchers in facts, computing device technology, information mining and laptop learning.

This booklet covers a much broader diversity of issues than a regular introductory textual content on mathematical facts. It contains smooth issues like nonparametric curve estimation, bootstrapping and type, themes which are frequently relegated to follow-up classes. The reader is thought to understand calculus and a bit linear algebra. No earlier wisdom of chance and records is needed. The textual content can be utilized on the complicated undergraduate and graduate level.

Larry Wasserman is Professor of data at Carnegie Mellon collage. he's additionally a member of the heart for computerized studying and Discovery within the institution of laptop technological know-how. His learn parts contain nonparametric inference, asymptotic idea, causality, and purposes to astrophysics, bioinformatics, and genetics. he's the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal–Statistical Society of Canada Prize in statistics. he's affiliate Editor of The magazine of the yank Statistical Association and The Annals of Statistics. he's a fellow of the yankee Statistical organization and of the Institute of Mathematical Statistics.

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Larry Wasserman's All of Statistics: A Concise Course in Statistical Inference PDF

This e-book is for those who are looking to research likelihood and facts speedy. It brings jointly some of the major rules in glossy data in a single position. The publication is appropriate for college kids and researchers in records, machine technological know-how, facts mining and computing device studying. This publication covers a wider variety of themes than a standard introductory textual content on mathematical facts.

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Extra resources for All of Statistics: A Concise Course in Statistical Inference

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2 Example. Let X '" Bernoulli(p). Then lE(X) = + (1 x p) = p. 3 Example. Flip a fair coin two times. Let X be the number of heads. Then, lE(X) = I xdFx(x) = Lx xfx(x) = (0 x f(O)) (0 x (1/4)) + (1 x (1/2)) + (2 x (1/4)) = 1. 4 Example. Let i I~l xdx = x'" + (1 x f(l)) Uniform( -1,3). Then, lE(X) = + (2 x f(2)) = I xdFx(x) = I xfx(x)dx = 1. 5 Example. Recall that a random variable has a Cauchy distribution if it has density fx(x) = {7f(1 + X2)}-1. Using integration by parts, (set u = x and v = tan- 1 x), J IxldF(x) = -2 7f ;'00 0 00 xdx = [x tan-1(x)jo l+x --2 100 0 tan- 1 xdx = 00 so the mean does not exist.

Let's construct a sample space explicitly for a Bernoulli random variable. Let D = [0,1] and define IF' to satisfy 1F'([a, b]) = b - a for a::; b::; 1. Fix p E [0,1] and define °: ; X(w) = { ~ w ::; p w > p. Then IF'(X = 1) = IF'(w ::; p) = 1F'([O,p]) = p and IF'(X = 0) = 1 - p. Thus, X rv Bernoulli(p). We could do this for all the distributions defined above. In practice, we think of a random variable like a random number but formally it is a mapping defined on some sample space. 4 Smne Irnportant Continuous Randmn Variables THE UNIFORM DISTRIBUTION.

13 Example. Suppose that X has f(x) Since f = 0 { 1 for Uniform (0,1). x PDF 1 (1+x)2 f(x)dx = 1, this is a well-defined for x < 0 otherwise. PDF . • Warning! Continuous random variables can lead to confusion. First, note that if X is continuous then JP'(X = x) = 0 for every x. Don't try to think of f(x) as JP'(X = x). This only holds for discrete random variables. We get probabilities from a PDF by integrating. A PDF can be bigger than 1 (unlike a mass function). For example, if f(x) = 5 for x E [0,1/5] and 0 otherwise, then f(x) ~ 0 and f f(x)dx = 1 so this is a well-defined PDF even though f(x) = 5 in some places.

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All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman


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