Skip to content

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

By Larry Wasserman

WINNER OF THE 2005 DEGROOT PRIZE!

This booklet is for those that are looking to research likelihood and records fast. It brings jointly the various major rules in sleek data in a single position. The e-book is acceptable for college kids and researchers in facts, desktop technological know-how, facts mining and laptop learning.

This booklet covers a wider variety of issues than a standard introductory textual content on mathematical records. It comprises smooth subject matters like nonparametric curve estimation, bootstrapping and class, issues which are often 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 complex undergraduate and graduate point.

Show description

Read Online or Download All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) PDF

Similar statistics books

The Black Swan: The Impact of the Highly Improbable (2nd Edition) (Incerto, Book 2)

The Black Swan is a standalone publication in Nassim Nicholas Taleb's landmark Incerto sequence, an research of opacity, success, uncertainty, chance, human mistakes, threat, and decision-making in an international we don't comprehend. the opposite books within the sequence are Fooled via Randomness, Antifragile, and The mattress of Procrustes.

Classic Topics on the History of Modern Mathematical Statistics

This booklet provides a transparent and accomplished consultant to the background of mathematical facts, together with information at the significant effects and an important advancements over a 2 hundred 12 months interval. the writer specializes in key historic advancements in addition to the controversies and disagreements that have been generated consequently.

Register-based Statistics in the Nordic Countries - Review of Best Practices with Focus on Population and Social Statistics

The Nordic international locations have an extended culture in utilizing administrative registers within the creation of respectable data. the target of this overview is to provide strategic and making plans officials within the nationwide Statistical Institutes an realizing of what register-based facts are, protecting additionally the required technical and administrative skill, and the prospective purposes of the how to produce professional facts.

Discrete Models of Financial Markets (Mastering Mathematical Finance)

This publication explains in uncomplicated settings the basic rules of monetary industry modelling and by-product pricing, utilizing the no-arbitrage precept. particularly user-friendly arithmetic results in robust notions and strategies - similar to viability, completeness, self-financing and replicating suggestions, arbitrage and identical martingale measures - that are at once acceptable in perform.

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

Sample text

37 Example. Let X and Y have a joint uniform distribution on the unit square. Thus, fXly(xly) = 1 for 0 <::: x <::: 1 and 0 otherwise. Given Y = y, X is Uniform(O, 1). • From the definition of the conditional density, we see that fx,Y(x, y) fXIY(xly)jy(y) = jylx(Ylx)fx(x). 39. 38 Example. Let f(x,y)= Let us find lP'(X y + (1/2). Hence, { < 1/41Y f X 0 +y if 0 <::: x <::: 1, 0 <::: y <::: 1 ' oth erWlse. 1/3). 27 we saw that jy(y) ( I ) - fx,y(x,y) XIY x y jy(y) So, 11/4 fxlY (x I 1 1/4 o X + 1.

THE BINOMIAL DISTRIBUTION. Suppose we have a coin which falls heads up with probability p for some 0 s: p s: 1. Flip the coin n times and let X be the number of heads. Assume that the tosses are independent. Let f(x) = J1D(X = x) be the mass function. It can be shown that f(x)= { (n) PX(l ox -p )n-x for x = 0, ... ,n otherwise. A random variable with this mass function is called a Binomial random variable and we write X rv Binomial(n,p). If Xl rv Binomial(nl,p) and X 2 rv Binomial(n2,p) then Xl +X2 rv Binomial(nl + n2,p).

If you simulate a Cauchy distribution many times and take the average, you will see that the average never settles down. This is because the Cauchy has thick tails and hence extreme observations are common. _ From now on, whenever we discuss expectations, we implicitly assume that they exist. Let Y = r(X). How do we compute lE(Y)? One way is to find Jy(y) and then compute lE(Y) = I yfy(y)dy. But there is an easier way. 6 Theorem (The Rule of the Lazy Statistician). Let Y = r(X). Then lE(Y) = lE(r(X)) = J r(x)dFx(x).

Download PDF sample

Rated 4.80 of 5 – based on 23 votes