Directional tests to compare nested parametric models are developed in the general context of covariance selection for Gaussian graphical models. The exactness of the underlying saddlepoint approximation leads to exceptional accuracy of the proposed approach. This is verified by simulation experiments with high-dimensional parameters of interest, where the accuracy of standard asymptotic approximations to the likelihood ratio test and some of its higher-order modifications fails. The directional p-value isused to illustrate the assessment of Markovian dependencies in a dataset from a veterinary trial on cattle. A second example with microarray data shows how to select the graph structure related to genetic anomalies due to acute lymphocytic leukemia.

翻译：用于比较嵌套参数模型的定向测试是在高西亚图形模型共变量选择的大背景下开发的。根基马鞍点近似值的准确性导致拟议方法的异常准确性。这通过高维相关参数的模拟实验得到验证,其中标准无症状近似值与概率比率测试的准确性及其某些较高顺序修改失败。方向P值用于说明对牛畜兽医试验数据集中Markovian依赖性的评估。第二个微粒数据实例显示如何选择与急性淋巴细胞性白血病有关的基因异常有关的图表结构。