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This article reviews several techniques useful for forming point and interval predictions in regression models with Box-Cox transformed variables. The techniques reviewed--plug-in, mean squared error analysis, predictive likelihood, and stochastic simulation--take account of nonnormality and parameter uncertainty in varying degrees. A Monte Carlo study examining their small-sample accuracy indicates that uncertainty about the Box-Cox transformation parameter may be…

Box–Cox 回归模型的预测方法AI 解读

This article reviews several techniques useful for forming point and interval predictions in regression models with Box-Cox transformed variables. The techniques reviewed--plug-in, mean squared error analysis, predictive likelihood, and stochastic simulation--take account of nonnormality and parameter uncertainty in varying degrees. A Monte Carlo study examining their small-sample accuracy indicates that uncertainty about the Box-Cox transformation parameter may be…