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Parametric bootstrap methods for bias correction in linear mixed models

Kubokawa, Tatsuya; Nagashima, Bui - In: Journal of Multivariate Analysis 106 (2012) C, pp. 1-16

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

Constrained empirical Bayes estimator and its uncertainty in normal linear mixed models

Kubokawa, Tatsuya - In: Journal of Multivariate Analysis 122 (2013) C, pp. 377-392

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

Bartlett-type adjustments for hypothesis testing in linear models with general error covariance matrices

Kojima, Masahiro; Kubokawa, Tatsuya - In: Journal of Multivariate Analysis 122 (2013) C, pp. 162-174

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

Modified conditional AIC in linear mixed models

Kawakubo, Yuki; Kubokawa, Tatsuya - In: Journal of Multivariate Analysis 129 (2014) C, pp. 44-56

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