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

By Larry Wasserman

WINNER OF THE 2005 DEGROOT PRIZE!

This e-book is for those that are looking to study likelihood and facts speedy. It brings jointly the various major principles in glossy data in a single position. The publication is acceptable for college students and researchers in statistics, desktop technology, facts mining and computer learning.

This booklet covers a much broader variety of themes than a regular introductory textual content on mathematical records. It comprises glossy themes like nonparametric curve estimation, bootstrapping and class, subject matters which are frequently relegated to follow-up classes. The reader is thought to grasp calculus and a bit linear algebra. No prior wisdom of chance and records is needed. The textual content can be utilized on the complicated undergraduate and graduate point.

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Additional info for All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)

Example text

The density of Z is g f(z) k f(zi) = 7 (27f~k/2 exp { -~ f;k zJ } (27f~k/2 exp { _~zT z } . We say that Z has a standard multivariate Normal distribution written Z rv N(O, I) where it is understood that 0 represents a vector of k zeroes and I is the k x k identity matrix. 10) fL)} where I~I denotes the determinant of ~, fL is a vector of length k and ~ is a k x k symmetric, positive definite matrix. 9 Setting fL = 0 and ~ = I gives back the standard Normal. Since ~ is symmetric and positive definite, it can be shown that there exists a matrix ~1/2 - called the square root of ~ - with the following properties: (i) ~1/2 is symmetric, (ii) ~ = ~1/2~1/2 and (iii) ~1/2~-1/2 = ~-1/2~1/2 = I where ~-1/2 = (~1/2)-1.

NORMAL (GAUSSIAN). 3) where fL E lR and (T > O. The parameter fL is the "center" (or mean) of the distribution and (T is the "spread" (or standard deviation) of the distribution. ) The Normal plays an important role in probability and statistics. Many phenomena in nature have approximately Normal distributions. Later, we shall study the Central Limit Theorem which says that the distribution of a sum of random variables can be approximated by a Normal distribution. We say that X has a standard Normal distribution if fL = 0 and (T = l.

Let X have 9. Let X rv CDF F. Find the CDF of X+ = max{O, X}. Exp(,6). Find F(x) and F-I(q). 10. Let X and Y be independent. Show that g(X) is independent of h(Y) where 9 and h are functions. 11. Suppose we toss a coin once and let p be the probability of heads. Let X denote the number of heads and let Y denote the number of tails. (a) Prove that X and Yare dependent. ) and suppose we toss a coin N times. Let X and Y be the number of heads and tails. Show that X and Yare independent. 12. 33. 13.

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