## Introduction to Mathematical StatisticsAn exceptionally clear and impeccably accurate presentation of statistical applications and more advanced theory. Included is a chapter on the distribution of functions of random variables as well as an excellent chapter on sufficient statistics. More modern technology is used in considering limiting distributions, making the presentations more clear and uniform. |

### From inside the book

Results 1-3 of 44

Page 2

under the same conditions, it is called a random experiment, and the collection of

every possible outcome is called the experimental space or the

Example 1. In the toss of a coin, let the outcome tails be denoted by T and let the

...

under the same conditions, it is called a random experiment, and the collection of

every possible outcome is called the experimental space or the

**sample space**.Example 1. In the toss of a coin, let the outcome tails be denoted by T and let the

...

Page 20

means, for our purposes, that the

are now confronted with the problem of defining a probability set function with C,

as the "new"

...

means, for our purposes, that the

**sample space**is effectively the subset C, . Weare now confronted with the problem of defining a probability set function with C,

as the "new"

**sample space**. Let the probability set function P(C) be defined on the...

Page 28

The

assumptions, compute the probability of each of these' ordered pairs. What is the

probability of at least one head? 1.5 Random Variables of the Discrete Type ...

The

**sample space**consists of four ordered pairs: TT, TH, HT, HH. Making certainassumptions, compute the probability of each of these' ordered pairs. What is the

probability of at least one head? 1.5 Random Variables of the Discrete Type ...

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Other editions - View all

### Common terms and phrases

Accordingly approximate best critical region chi-square distribution complete sufficient statistic conditional p.d.f. conditional probability confidence interval Consider continuous type converges in probability correlation coefficient critical region defined degrees of freedom denote a random depend upon 9 discrete type distribution function F(x distribution with mean distribution with p.d.f. distribution with parameters equation estimator of 9 Example Exercise F-distribution gamma distribution given H0 is true hypothesis H0 independent random variables integral joint p.d.f. Let the random Let Xu X2 limiting distribution marginal p.d.f. matrix moment-generating function order statistics p.d.f. of Xu percent confidence interval Poisson distribution positive integer probability density functions probability set function quadratic form random experiment random sample random variables Xx reject H0 respectively sample space Section Show significance level simple hypothesis statistic for 9 sufficient statistic testing H0 theorem unbiased estimator variance a2 Xx and X2 Yu Y2 zero elsewhere