Journal
STATISTICS AND COMPUTING
Volume 20, Issue 3, Pages 295-303Publisher
SPRINGER
DOI: 10.1007/s11222-009-9122-2
Keywords
Best linear unbiased predictor; h-likelihood; Hierarchical generalized linear models; Marginal likelihood; Prediction interval; Random effects
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Recently, the orthodox best linear unbiased predictor (BLUP) method was introduced for inference about random effects in Tweedie mixed models. With the use of h-likelihood, we illustrate that the standard likelihood procedures, developed for inference about fixed unknown parameters, can be used for inference about random effects. We show that the necessary standard error for the prediction interval of the random effect can be computed from the Hessian matrix of the h-likelihood. We also show numerically that the h-likelihood provides a prediction interval that maintains a more precise coverage probability than the BLUP method.
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