论文标题
有条件的分配模型规范测试使用卡方拟合优度测试
Conditional Distribution Model Specification Testing Using Chi-Square Goodness-of-Fit Tests
论文作者
论文摘要
本文介绍了卡方拟合测试，以检查有条件的分配模型规范。根据因变量和解释变量的Rosenblatt变换进行了交叉分类，从而产生了一个应急表，其预期关节频率等于行和柱边缘的乘积，这些频率与模型参数无关。测试统计数据评估观察到的频率和预期频率之间的差异是否是由于机会造成的。我们提出了三种类型的测试统计数据：基于分组数据的可能性的经典三位一体，以及基于有效的原始数据估计器的两个统计数据 - 即Chernoff-Lehmann和广义的Wald统计量。这些统计数据的渐近分布对于样品依赖性分区是不变的。蒙特卡洛实验证明了提出的测试的良好性能。
This paper introduces chi-square goodness-of-fit tests to check for conditional distribution model specification. The data is cross-classified according to the Rosenblatt transform of the dependent variable and the explanatory variables, resulting in a contingency table with expected joint frequencies equal to the product of the row and column marginals, which are independent of the model parameters. The test statistics assess whether the difference between observed and expected frequencies is due to chance. We propose three types of test statistics: the classical trinity of tests based on the likelihood of grouped data, and two statistics based on the efficient raw data estimator -- namely, a Chernoff-Lehmann and a generalized Wald statistic. The asymptotic distribution of these statistics is invariant to sample-dependent partitions. Monte Carlo experiments demonstrate the good performance of the proposed tests.
