论文标题
K-2旋转用于多元数据的合适性
K-2 rotated goodness-of-fit for multivariate data
论文作者
论文摘要
考虑一组多元分布，$ f_1，\ dots，f_m $，旨在解释相同的现象。例如，每个$ f_m $可能对应于用于校准数据的不同候选背景模型，或我们旨在在实验数据上验证的许多可能的信号模型之一。在本文中，我们表明，可以将一类明显不同的模型$ f_ {m} $的测试映射到单个测试中，以进行参考分布$ q $。结果，可以通过模拟\ undesline {仅} $ q $下的测试统计量的分布来获得每个$ f_m $的有效推断。此外，可以方便地选择$ Q $，以实质上减少计算时间。
Consider a set of multivariate distributions, $F_1,\dots,F_M$, aiming to explain the same phenomenon. For instance, each $F_m$ may correspond to a different candidate background model for calibration data, or to one of many possible signal models we aim to validate on experimental data. In this article, we show that tests for a wide class of apparently different models $F_{m}$ can be mapped into a single test for a reference distribution $Q$. As a result, valid inference for each $F_m$ can be obtained by simulating \underline{only} the distribution of the test statistic under $Q$. Furthermore, $Q$ can be chosen conveniently simple to substantially reduce the computational time.
