Predicting multivariate responses in multiple linear regression
Predicting multivariate responses in multiple linear regression
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DOI：
10.1111/1467-9868.00054
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发表时间：
1997-01-01
期刊：
影响因子：
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通讯作者：
Friedman, JH
中科院分区：
文献类型：
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作者：
Breiman, L;Friedman, JH
我们来看看从同一组解释变量中预测几个响应变量的问题。问题是如何利用响应变量之间的相关性，以提高预测精度相比，通常的程序，做单独的回归每个响应变量对共同的预测变量集。介绍了一种新的程序，称为凝乳和乳清法。当响应之间存在相关性时，它的使用可以大大减少预测误差，同时即使响应不相关也能保持准确性。在广泛的模拟，新的程序进行比较，与以前提出的方法预测多个响应（包括偏最小二乘），并表现出上级的准确性。其中一个版本可以在标准统计程序包中轻松实施。
We look at the problem of predicting several response variables from the same set of explanatory variables. The question is how to take advantage of correlations between the response variables to improve predictive accuracy compared with the usual procedure of doing individual regressions of each response variable on the common set of predictor variables. A new procedure is introduced called the curds and whey method. Its use can substantially reduce prediction errors when there are correlations between responses while maintaining accuracy even if the responses are uncorrelated. In extensive simulations, the new procedure is compared with several previously proposed methods for predicting multiple responses (including partial least squares) and exhibits superior accuracy. One version can be easily implemented in the context of standard statistical packages.