Introduction to Mathematical Statistics |
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Page 188
... distribution function . Moreover , lim Fn ( y ) continuity of F ( y ) . In accordance with the definition of a limiting distribu- tion , the random variable Y has a ... limiting distributions , if they 188 Limiting Distributions [ Ch . 7.
... distribution function . Moreover , lim Fn ( y ) continuity of F ( y ) . In accordance with the definition of a limiting distribu- tion , the random variable Y has a ... limiting distributions , if they 188 Limiting Distributions [ Ch . 7.
Page 190
... limiting distribution with distribution function G ( z ) . This affords us an example of a limiting distribution that is not degenerate . EXERCISES 7.1 . Let the p.d.f. of Y be fn ( y ) = Y does not have a limiting distribution . 1 , y ...
... limiting distribution with distribution function G ( z ) . This affords us an example of a limiting distribution that is not degenerate . EXERCISES 7.1 . Let the p.d.f. of Y be fn ( y ) = Y does not have a limiting distribution . 1 , y ...
Page 194
... limit is et2 12 . Example 1. Let Y have a distribution which is b ( n , p ) . Suppose that the mean μ = np is the same for every n ; that is , p μ / n , where μ is a constant . We shall find the limiting distribution of the binomial ...
... limit is et2 12 . Example 1. Let Y have a distribution which is b ( n , p ) . Suppose that the mean μ = np is the same for every n ; that is , p μ / n , where μ is a constant . We shall find the limiting distribution of the binomial ...
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A₁ A₂ accept accordance Accordingly alternative approximately assume called cent Chapter complete compute Consider constant continuous type critical region decision defined definition degrees of freedom denote a random depend determine discrete type distribution function equal Equation event Example EXERCISES exists expected fact given H₁ Hence hypothesis inequality integral interval joint p.d.f. Let X1 likelihood marginal matrix maximum mean moment-generating function mutually stochastically independent normal distribution Note observed order statistics parameter probability density functions problem proof prove random experiment random interval random sample random variable ratio reject respectively result sample space Show significance level simple hypothesis stochastically independent sufficient statistic symmetric matrix Table theorem transformation true unknown variables X1 variance W₁ X₁ X₂ Y₁ Y₂ zero elsewhere μ₁