## Introduction to Mathematical Statistics |

### From inside the book

Results 1-3 of 39

Page 108

In the first place, to say that f(z; 6), y < 0 < 6, is a complete p.c.f. really means that

we are not talking about one p.d.f. but instead about a whole family of

In the first place, to say that f(z; 6), y < 0 < 6, is a complete p.c.f. really means that

we are not talking about one p.d.f. but instead about a whole family of

**probability****density functions**. For each fixed number 6, y < 0 < 6, there corresponds one ...Page 110

In this sense [that is, b(yi) = p(yi), at all points of nonzero

is the unique continuous

accordance with the Rao-Blackwell theorem, 5(Y) has a smaller variance than

every ...

In this sense [that is, b(yi) = p(yi), at all points of nonzero

**probability density**], p(Yi)is the unique continuous

**function**of Yi that is an unbiased statistic for 0. Inaccordance with the Rao-Blackwell theorem, 5(Y) has a smaller variance than

every ...

Page 244

power, 168, 169

4 Hypothesis, see Statistical hypothesis Jacobian, 67, 78 Joint

...

**Function**, continued likelihood, 118, 121 moment-generating, 21 point, 4, 137power, 168, 169

**probability density**, 13, 14 probability set, 9 regression, 212 set,4 Hypothesis, see Statistical hypothesis Jacobian, 67, 78 Joint

**probability density**...

### 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