Introduction to Mathematical Statistics |
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Page v
... Chapters One and Two . In Chapter Three , we take up stochastic independence and random sam- pling . The concepts of sufficiency and completeness are introduced in Chapter Five and are used to serve as a basis for a theory of point ...
... Chapters One and Two . In Chapter Three , we take up stochastic independence and random sam- pling . The concepts of sufficiency and completeness are introduced in Chapter Five and are used to serve as a basis for a theory of point ...
Page viii
... CHAPTER FIVE VPOINT ESTIMATION 5.1 . Conditional Distributions 5.2 . The Problem of Point Estimation 5.3 . A Sufficient Statistic for a Parameter 5.4 . The Fisher - Neyman Criterion 5.5 . The Rao - Blackwell Theorem 00 90 95 96 101 104 ...
... CHAPTER FIVE VPOINT ESTIMATION 5.1 . Conditional Distributions 5.2 . The Problem of Point Estimation 5.3 . A Sufficient Statistic for a Parameter 5.4 . The Fisher - Neyman Criterion 5.5 . The Rao - Blackwell Theorem 00 90 95 96 101 104 ...
Page 242
... Chapter Seven 7.1 . 3x22x3 , 0 < x < 1 . 7.10 . 0.840 . 7.12 . 160 . 7.3 . ( a ) 27/32 ; ( b ) 1/24 . 7.14 . 0.08 . 7.16 . 0.267 . Chapter Eight et 1 - 8.4 . t0 . 8.5 . n !, 0 < 1 < 22 < ... < 2n < 1 . T 1 8.8 . n + n + 1 8.9 . n !, 0 ...
... Chapter Seven 7.1 . 3x22x3 , 0 < x < 1 . 7.10 . 0.840 . 7.12 . 160 . 7.3 . ( a ) 27/32 ; ( b ) 1/24 . 7.14 . 0.08 . 7.16 . 0.267 . Chapter Eight et 1 - 8.4 . t0 . 8.5 . n !, 0 < 1 < 22 < ... < 2n < 1 . T 1 8.8 . n + n + 1 8.9 . n !, 0 ...
Contents
CHAPTER | 1 |
TRANSFORMATION OF VARIABLES cont | 17 |
CHAPTER | 27 |
Copyright | |
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Common terms and phrases
A₁ A₂ best critical region binomial distribution c₁ cent confidence interval chi-square distribution composite hypothesis conditional p.d.f. confidence interval Consider continuous type critical region degrees of freedom denote a random discrete type distribution function distribution having p.d.f. distribution with mean EXAMPLE Exercises function of Y₁ hypothesis H₁ independent random variables integral Jacobian joint p.d.f. joint sufficient statistics Let the random Let X1 Let Y₁ limiting distribution marginal p.d.f. moment-generating function mutually stochastically independent Mx(t My(t normal distribution n(x null hypothesis null simple hypothesis one-to-one transformation order statistics p.d.f. of Y₁ p₁ Poisson distribution positive integer Pr(a Pr(X Pr(Y probability density functions r₁ random experiment random sample random variables X1 Show significance level statistical hypothesis stochastically independent random theorem type of random unbiased statistic values variance o² X₁ X1 and X2 X₂ Xn denote Y₂ zero elsewhere μ₁ σ²