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- Best_linear_unbiased_prediction abstract "In statistics, best linear unbiased prediction (BLUP) is used in linear mixed models for the estimation of random effects. BLUP was derived by Charles Roy Henderson in 1950 but the term "best linear unbiased predictor" (or "prediction") seems not to have been used until 1962. "Best linear unbiased predictions" (BLUPs) of random effects are similar to best linear unbiased estimates (BLUEs) (see Gauss–Markov theorem) of fixed effects. The distinction arises because it is conventional to talk about estimating fixed effects but predicting random effects, but the two terms are otherwise equivalent. (This is a bit strange since the random effects have already been "realized" − they already exist. The use of the term "prediction" may be because in the field of animal breeding in which Henderson worked, the random effects were usually genetic merit, which could be used to predict the quality of offspring (Robinson page 28). However, the equations for the "fixed" effects and for the random effects are different.In practice, it is often the case that the parameters associated with the random effect(s) term(s) are unknown; these parameters are the variances of the random effects and residuals. Typically the parameters are estimated and plugged into the predictor, leading to the Empirical Best Linear Unbiased Predictor (EBLUP). Notice that by simply plugging in the estimated parameter into the predictor, additional variability is unaccounted for, leading to overly optimistic prediction variances for the EBLUP.Best linear unbiased predictions are similar to empirical Bayes estimates of random effects in linear mixed models, except that in the latter case, where weights depend on unknown values of components of variance, these unknown variances are replaced by sample-based estimates.".
- Best_linear_unbiased_prediction wikiPageExternalLink .Ui4zjWRgYXc.
- Best_linear_unbiased_prediction wikiPageExternalLink rrblup-package-in-r-for-genomewide-selection.
- Best_linear_unbiased_prediction wikiPageExternalLink genomic-relationships-and-gblup.
- Best_linear_unbiased_prediction wikiPageID "15915709".
- Best_linear_unbiased_prediction wikiPageRevisionID "586950105".
- Best_linear_unbiased_prediction hasPhotoCollection Best_linear_unbiased_prediction.
- Best_linear_unbiased_prediction subject Category:Estimation_theory.
- Best_linear_unbiased_prediction subject Category:Statistical_methods.
- Best_linear_unbiased_prediction type Ability105616246.
- Best_linear_unbiased_prediction type Abstraction100002137.
- Best_linear_unbiased_prediction type Cognition100023271.
- Best_linear_unbiased_prediction type Know-how105616786.
- Best_linear_unbiased_prediction type Method105660268.
- Best_linear_unbiased_prediction type PsychologicalFeature100023100.
- Best_linear_unbiased_prediction type StatisticalMethod106020737.
- Best_linear_unbiased_prediction type StatisticalMethods.
- Best_linear_unbiased_prediction comment "In statistics, best linear unbiased prediction (BLUP) is used in linear mixed models for the estimation of random effects. BLUP was derived by Charles Roy Henderson in 1950 but the term "best linear unbiased predictor" (or "prediction") seems not to have been used until 1962. "Best linear unbiased predictions" (BLUPs) of random effects are similar to best linear unbiased estimates (BLUEs) (see Gauss–Markov theorem) of fixed effects.".
- Best_linear_unbiased_prediction label "BLUP".
- Best_linear_unbiased_prediction label "Best linear unbiased prediction".
- Best_linear_unbiased_prediction label "最佳线性无偏预测".
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