In this work, we propose an automatic method for the analysis of experiments that incorporates hierarchical relationships between the experimental variables. We use a modified version of nonnegative garrote method for variable selection which can incorporate hierarchical relationships. The nonnegative garrote method requires a good initial estimate of the regression parameters for it to work well. To obtain the initial estimate, we use generalized ridge regression with the ridge parameters estimated from a Gaussian process prior placed on the underlying input-output relationship. The proposed method, called HiGarrote, is fast, easy to use, and requires no manual tuning. Analysis of several real experiments are presented to demonstrate its benefits over the existing methods.

翻译：本文提出了一种自动分析实验的方法，该方法融入了实验变量间的层次关系。我们采用改进的非负套索方法进行变量选择，该方法能够整合层次关系。非负套索方法需要回归参数的优质初始估计才能良好工作。为获得初始估计，我们采用广义岭回归，其岭参数通过置于底层输入-输出关系上的高斯过程先验进行估计。所提出的方法（称为HiGarrote）具有快速、易用且无需手动调参的特点。通过对多个真实实验的分析，展示了该方法相较于现有方法的优势。