Ridge regression in prediction problems: automatic choice of the ridge parameter.
Ridge regression in prediction problems: automatic choice of the ridge parameter.
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DOI：
10.1002/gepi.21750
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发表时间：
2013-11
影响因子：
2.1
通讯作者：
De Iorio, Maria
中科院分区：
文献类型：
--
作者：
Cule, Erika;De Iorio, Maria
迄今为止，许多遗传变异已被确定为与不同的表型性状相关。然而，已确定的关联通常只能解释性状遗传性的一小部分，并且仅包含已知相关变异的模型的预测能力很小。多元回归是一种流行的框架，它同时考虑许多遗传变异的联合效应。由于遗传数据的高维数和预测因子之间的相关结构，普通多元回归很少适用于遗传数据。人们重新对使用惩罚性回归技术来规避这些困难产生了兴趣。在本文中，我们关注岭回归，这是一种惩罚回归方法，已被证明在多元预测问题中提供良好的性能。脊回归应用中的一个挑战是选择控制回归系数收缩量的脊参数。我们提出了一种基于数据确定脊参数的方法，目的是在高维预测问题中获得良好的性能。我们建立了该方法的理论依据，并在模拟遗传数据和实际数据实例上验证了其性能。同时，将脊回归模型拟合到数十万到数百万的遗传变异中提出了计算上的挑战。我们开发了一个R包ridge来解决这些问题。Ridge实现了本文提出的山脊参数的自动选择，并且可以从CRAN中免费获得。
To date, numerous genetic variants have been identified as associated with diverse phenotypic traits. However, identified associations generally explain only a small proportion of trait heritability and the predictive power of models incorporating only known-associated variants has been small. Multiple regression is a popular framework in which to consider the joint effect of many genetic variants simultaneously. Ordinary multiple regression is seldom appropriate in the context of genetic data, due to the high dimensionality of the data and the correlation structure among the predictors. There has been a resurgence of interest in the use of penalised regression techniques to circumvent these difficulties. In this paper, we focus on ridge regression, a penalised regression approach that has been shown to offer good performance in multivariate prediction problems. One challenge in the application of ridge regression is the choice of the ridge parameter that controls the amount of shrinkage of the regression coefficients. We present a method to determine the ridge parameter based on the data, with the aim of good performance in high-dimensional prediction problems. We establish a theoretical justification for our approach, and demonstrate its performance on simulated genetic data and on a real data example. Fitting a ridge regression model to hundreds of thousands to millions of genetic variants simultaneously presents computational challenges. We have developed an R package, ridge, which addresses these issues. Ridge implements the automatic choice of ridge parameter presented in this paper, and is freely available from CRAN.
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影响因子：
30.8
作者：
Marchini, Jonathan;Howie, Bryan;Donnelly, Peter
通讯作者：
Donnelly, Peter
影响因子：
2.5
作者：
HOERL, AE;KENNARD, RW
通讯作者：
KENNARD, RW
影响因子：
3.3
作者：
de Los Campos G;Hickey JM;Pong-Wong R;Daetwyler HD;Calus MP
通讯作者：
Calus MP
影响因子：
4.5
作者：
Hoggart, Clive J.;Whittaker, John C.;De Iorio, Maria;Balding, David J.
通讯作者：
Balding, David J.
影响因子：
2.7
作者：
ANDERSON, JA
通讯作者：
ANDERSON, JA