如何访问Sklearn的KDE参数用于SCIPY的Kolmogorov-Smirnov测试? 我有一个一维离散数据集。在这个集合上,我想用Sklearn的内置函数执行一个内核密度估计: from sklearn.neighbors.kde import KernelDensity data = ...# array of shape [5000, 1]## perform kde with
The Kolmogorov–Smirnov statistic for testing Eq. (5.3) is based on max|F^(x)−G^(x)|, the maximum being taken over all possible values of x. That is, the test statistic is based on an estimate of the Kolmogorov distance between the two distributions. Let Zi be the n + m pool...
We introduce a process blend of an empirical one and kernel method. We prove the convergence of this process to a Gaussian process. From this result, we can get the limit distribution of a family of test's statistics and in particular one similar to Kolmogorov-Smirnov test....
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In the case of the photopyroelectric signal obtained from a maize seed with crystalline structure, it was not possible to describe statistically the amplitude variations of the signal by means of the transformed Moyal distribution because it did not pass the Kolmogorov–Smirnov test, so the same ...
Kolmogorov–Smirnov Maximum Discrepancy Calculation Empty CellCDFDifference TimeF(T) TheoreticalF(T) Experimental 0 0.000 0.000 0.000 1 0.330 0.109 0.220 2 0.551 0.266 0.285 3 0.699 0.422 0.277 4 0.798 0.578 0.220 5 0.865 0.734 0.130 6 0.909 0.891 0.019 CDF, Cumulative density function. Column 1...