Classified generalized linear mixed model prediction incorporating pseudo-prior information

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY
Haiqiang Ma, Jiming Jiang
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引用次数: 0

Abstract

We develop a method of classified mixed model prediction based on generalized linear mixed models that incorporate pseudo-prior information to improve prediction accuracy. We establish consistency of the proposed method both in terms of prediction of the true mixed effect of interest and in terms of correctly identifying the potential class corresponding to the new observations if such a class matching one of the training data classes exists. Empirical results, including simulation studies and real-data validation, fully support the theoretical findings.

包含伪先验信息的分类广义线性混合模型预测
我们开发了一种基于广义线性混合模型的分类混合模型预测方法,该方法结合了伪先验信息以提高预测精度。我们建立了所提出的方法的一致性,无论是在预测感兴趣的真实混合效应方面,还是在正确识别与新观察相对应的潜在类别方面,如果存在与训练数据类别之一匹配的类别的话。实证结果,包括模拟研究和真实数据验证,完全支持理论发现。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
62
审稿时长
>12 weeks
期刊介绍: The Canadian Journal of Statistics is the official journal of the Statistical Society of Canada. It has a reputation internationally as an excellent journal. The editorial board is comprised of statistical scientists with applied, computational, methodological, theoretical and probabilistic interests. Their role is to ensure that the journal continues to provide an international forum for the discipline of Statistics. The journal seeks papers making broad points of interest to many readers, whereas papers making important points of more specific interest are better placed in more specialized journals. The levels of innovation and impact are key in the evaluation of submitted manuscripts.
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