Get All of Statistics: A Concise Course in Statistical Inference PDF

By Larry Wasserman

ISBN-10: 0387217363

ISBN-13: 9780387217369

ISBN-10: 1441923225

ISBN-13: 9781441923226

This e-book is for those that are looking to study chance and records fast. It brings jointly some of the major principles in sleek facts in a single position. The ebook is acceptable for college kids and researchers in statistics, computing device technological know-how, information mining and computing device learning.

This publication covers a much broader variety of subject matters than a regular introductory textual content on mathematical information. It comprises glossy issues like nonparametric curve estimation, bootstrapping and category, subject matters which are often relegated to follow-up classes. The reader is thought to understand calculus and a bit linear algebra. No past wisdom of likelihood and data is needed. The textual content can be utilized on the complex undergraduate and graduate level.

Larry Wasserman is Professor of records at Carnegie Mellon collage. he's additionally a member of the heart for computerized studying and Discovery within the college of machine technology. His study components comprise nonparametric inference, asymptotic conception, 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 facts. he's affiliate Editor of The magazine of the yank Statistical Association and The Annals of Statistics. he's a fellow of the yank Statistical organization and of the Institute of Mathematical Statistics.

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

Example text

Let F be the 1. JP'(X = CDF (1+x) = for x < 0 otherwise. fooo dx/(l +x) = J~oo du/u = log(oo) for a random variable X. Then: F(x) - F(x-) where F(x-) = limytx F(y); = 00 . 3 Some Important Discrete Random Variables 2. lP'(x x) = 1 - F(x); 4. If X is continuous then F(b) - F(a) lP'(a lP'(a It is also useful to define the inverse < < X < b) = lP'(a ::; X < b) X ::; b) = lP'(a ::; X::; b). CDF (or quantile function). 16 Definition. Let X be a random variable with CDF F.

The parameter p is usually unknown and must be estimated from data; that's what statistical inference is all about. In most statistical models, there are random variables and parameters: don't confuse them. THE GEOMETRIC DISTRIBUTION. X has a geometric distribution with parameter p E (0, 1), written X rv Geom (p), if J1D(X = k) = p(l - p)k-\ k:;:, 1. 4 Some Important Continuous Random Variables 27 We have that Think of X as the number of flips needed until the first head when flipping a coin. THE POISSON DISTRIBUTION.

Hence, Fx(x) = J~T Let Y = r(X) = logX. Then, Ay = {x: x:::; eY} and Fy(y) lP'(Y :::; y) = lP'(logX :::; y) lP'(X :::; eY) = Fx(e Y) = 1 - e-e". Therefore, fy(y) = eYe-e Y for y E R _ fx(s)ds = 42 2. 47 Example. Let X density of X is Uniform ( -1,3). Find the rv fx(x) = {1/4 o if - 1 ~ x < otherwlse. PDF of Y 3 Y can only take values in (0,9). Consider two cases: (i) 0 < y < 1 and (ii) 1 <::: y < 9. For case (i), Ay = [-JY, JYl and Fy(y) = fA fx(x)dx = (1/2)JY. For case (ii), Ay = [-1, JYl and Fy(y) = J~ y fx(x)dx = (1/4)(JY + 1).

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


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