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Inference for variance components in linear mixed models of ANOVA type, including estimation and testing, has been investigated when the number of fixed effects is fixed. However, for high-dimensional data, this number is large and would be regarded as a divergent value as the sample size goes...
Persistent link: https://www.econbiz.de/10011116228
resulting empirical best linear unbiased prediction (EBLUP) estimator of a small area mean from the possible over-shrinking to …-order unbiased mean square error (MSE) estimate of an EBLUP. In this paper, we propose a new adjustment factor that rectifies the …
Persistent link: https://www.econbiz.de/10010737764
In linear mixed models, the conditional Akaike Information Criterion (cAIC) is a procedure for variable selection in light of the prediction of specific clusters or random effects. This is useful in problems involving prediction of random effects such as small area estimation, and much attention...
Persistent link: https://www.econbiz.de/10010786423
The empirical best linear unbiased predictor (EBLUP) in the linear mixed model (LMM) is useful for the small area … estimation, and the estimation of the mean squared error (MSE) of EBLUP is important as a measure of uncertainty of EBLUP. To … partial derivatives of some quantities. A similar difficulty occurs in the construction of confidence intervals based on EBLUP …
Persistent link: https://www.econbiz.de/10010576499
The empirical Bayes (EB) estimator or empirical best linear unbiased predictor (EBLUP) in the linear mixed model (LMM …
Persistent link: https://www.econbiz.de/10010702801
Consider the problem of testing a linear hypothesis of regression coefficients in a general linear regression model with a covariance matrix involving several nuisance parameters. Then, the Bartlett-type adjustments of the Wald, Score, and modified Likelihood Ratio tests are derived for general...
Persistent link: https://www.econbiz.de/10010702805
In the M-estimation theory developed by Huber (1964, Ann. Math. Statist.43, 1449-1458), the parameter under estimation is the value of [theta] which minimizes the expectation of what is called a discrepancy measure (DM) [delta](X, [theta]) which is a function of [theta] and the underlying...
Persistent link: https://www.econbiz.de/10005152937