Using regression models for prediction: shrinkage and regression to the mean.
Using regression models for prediction: shrinkage and regression to the mean.
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DOI：
10.1191/096228097667367976
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发表时间：
1997-06-01
影响因子：
2.3
通讯作者：
Copas, J B
中科院分区：
文献类型：
--
作者：
Copas, J B
使用拟合的回归模型预测未来的情况下，无论是作为一种诊断工具，或作为一种工具进行风险评估进行了讨论。对均值效应的回归意味着响应变量的未来值往往比预测值的预期值更接近总体均值。这种收缩的程度进行了研究的多元和逻辑回归模型，并发现与简单的拟合优度统计的原始回归。如果样本量小和/或协变量的数量大，收缩是一个特别严重的问题。通过两个例子说明了预测量的收缩。提出了一个更一般的提法。
The use of a fitted regression model in predicting future cases, either as a diagnostic tool or as an instrument for risk assessment is discussed. The regression to the mean effect implies that the future values of the response variable tend to be closer to the overall mean than might be expected from the predicted values. The extent of this shrinkage is studied for multiple and logistic regression models, and is found to be related to simple goodness-of-fit statistics of the original regression. Shrinkage is a particularly serious problem if the sample size is small and/or the number of covariates is large. Shrinkage of predictors is illustrated by two examples. A more general formulation is suggested.