A one-sample test for normality with kernel methods

A Kernel Two-Sample Test

We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space (RKHS), and is called the maximum mean discrepancy (MMD). We present two distributionfre...

A Fast, Consistent Kernel Two-Sample Test

A kernel embedding of probability distributions into reproducing kernel Hilbert spaces (RKHS) has recently been proposed, which allows the comparison of two probability measures P and Q based on the distance between their respective embeddings: for a sufficiently rich RKHS, this distance is zero if and only if P and Q coincide. In using this distance as a statistic for a test of whether two sam...

Identification and Ranking Green Supplier Selection Criteria Using One-Sample T-Test and FANP Methods: A Case Study for Petrochemical Industry

Increasing global notices in environmental protection, green supply chain management (GSCM) has received much attention by researchers and managers more than the past. Commonly, firms have considered cost criteria to select their suppliers. Despite the fact that there are various papers considering the formal criteria in supplier selection, there is a few limited numbers considering the environ...

New Graphical Methods and Test Statistics for Testing Composite Normality

Several graphical methods for testing univariate composite normality from an i.i.d. sample are presented. They are endowed with correct simultaneous error bounds and yield size-correct tests. As all are based on the empirical CDF, they are also consistent for all alternatives. For one test, called the modified stabilized probability test, or MSP, a highly simplified computational method is deri...

Asymptotic Normality and Robustness of One Sample Chernoff - Savage Statistics