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Prediction-Interval Procedures and (Fixed-Effects) Confidence-Interval Procedures for Mixed Linear Models

机译：混合线性模型的预测 - 区间程序和（固定效应）置信区间程序

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A general approach is presented for devising an approximate 100(1-alpha)% prediction interval for an unobservable random variable w based on the value of an observable random vector y. It is assumed that E(w) and E(y) are linear combinations of unknown parameters beta1, beta2, ..., betap and that the joint distribution of w-E(w) and y-E(y) is symmetric and known up to the value of a vector theta of unknown parameters, as would be the case if y followed a mixed linear model (with normally distributed random effects and errors) and w were a linear combination of the model's fixed and random effects. Various implementations of the proposed approach are illustrated (and comparisons among them made) in the context of the Behrens-Fisher problem and the problem of making inferences about a group mean under a balanced one-way random-effects model. Keywords: Behrens-Fisher problem, Reprints. (KR)

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