## Introduction to Mathematical Statistics |

### From inside the book

Results 1-3 of 66

Page 40

Sections 2.1 and 2.2, certain constants enter the p.d.f. f(z). In the binomial

distribution, these constants were a positive integer n and a number p, 0 < p < 1;

in the ...

**Parameters**of a Distribution. In each of the four special distributions introduced inSections 2.1 and 2.2, certain constants enter the p.d.f. f(z). In the binomial

distribution, these constants were a positive integer n and a number p, 0 < p < 1;

in the ...

Page 90

CHAPTER FIVE POINT ESTIMATION In some of the preceding chapters we have

considered the problem of estimating certain

means of a collection of sample values. In each instance, we first determined a ...

CHAPTER FIVE POINT ESTIMATION In some of the preceding chapters we have

considered the problem of estimating certain

**parameters**of distributions bymeans of a collection of sample values. In each instance, we first determined a ...

Page 95

Then compute E(Y|Yaya). 5.2. The Problem of Point Estimation. Suppose a

distribution has a p.d.f. of known functional form, but it involves a certain unknown

indicated ...

Then compute E(Y|Yaya). 5.2. The Problem of Point Estimation. Suppose a

distribution has a p.d.f. of known functional form, but it involves a certain unknown

**parameter**6 that may have any value in an interval of values. This will beindicated ...

### 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 best critical region binomial distribution cent confidence interval chi-square distribution composite hypothesis conditional p.d.f. confidence interval Consider continuous type degrees of freedom denote a random discrete type distribution function distribution having p.d.f. distribution with mean ExAMPLE Exercises f(zi function F(z hypothesis H1 independent random variables integral Jacobian joint p.d.f. joint sufficient statistics Let the random Let X1 limiting distribution marginal p.d.f. moment-generating function mutually stochastically independent Mx(t My(t normal distribution n(z null hypothesis null simple hypothesis one-to-one transformation order statistics p.d.f. of Yi Poisson distribution positive integer Pr(a Pr(X Pr(Y probability density functions quadratic form random experiment random interval random sample random variables X1 respectively ſ ſ sample space Show significance level ſº stochastically independent random subset sufficient statistic Yi theorem tion type of random unbiased statistic values X1 and X2 Xn denote zero elsewhere