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A less extreme solution would be to specify some information about B , e . g . , we would assume that B was a random variable with a prior probability distribution ( independent of E ) , with mean and variance given , say , by EB = B ...
A less extreme solution would be to specify some information about B , e . g . , we would assume that B was a random variable with a prior probability distribution ( independent of E ) , with mean and variance given , say , by EB = B ...
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Hence , a " best " ( or minimum varia nce ) unbia- sed linear estimator of B in the linear regression model ( 1 ) is a linear estimator ( 5 ) which has smallest variance AVA ' among all those linear unbiased estimators .
Hence , a " best " ( or minimum varia nce ) unbia- sed linear estimator of B in the linear regression model ( 1 ) is a linear estimator ( 5 ) which has smallest variance AVA ' among all those linear unbiased estimators .
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Zacks , S. , and M. Even , : Minimum Variance Unbiased and Maximum Likelihood Estimators of Reliability Functions for Systems in Series and Parallel , J. Amer . Stat . Assoc . Vol . 61 , 1966 . 5. Pearson , K. , : Tables of the ...
Zacks , S. , and M. Even , : Minimum Variance Unbiased and Maximum Likelihood Estimators of Reliability Functions for Systems in Series and Parallel , J. Amer . Stat . Assoc . Vol . 61 , 1966 . 5. Pearson , K. , : Tables of the ...
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