Competitors of the Kendall-Tau Test for Testing Independence Against Positive Quadrant Dependence

On Testing Against Positive Quadrant Dependence Based on Sub-Sample Order Statistics

“…For testing independence against PQD, Kochar and Gupta [6,7] proposed some competitors of Kendall's sample tau coefficient. Schriever [13] contained a large number of tests, available in the literature for the problem of independence.…”

A Class of Distribution-Free Tests for Independence Against Positive Quadrant Dependence

“…Obviously U 2 is the celebrated Kendall's tau statistic. Kochar and Gupta (1987) have proposed tests based on U statistics associated with kernels  1k for the above problem in the case of skewed alternatives. It is expected that the newly proposed tests will be quite efficient for the symmetric case.…”

“…The class of tests proposed by Kochar and Gupta (1987) and Kendall's test are members of the proposed class. The performance of the proposed class is evaluated in terms of Pitman asymptotic relative efficiency for Block-Basu (1974) model and Woodworth family of distributions.…”

“…Moreover the alternative H 1 are ordered alternatives and this gives rise to general difficulties associated with ordered restricted inference. Kochar andGupta (1987, 1990) proposed some competitors of Kendall's sample tau coefficient for testing independence against PQD. Shriever (1987) contains a list of large number of tests available in the literature for the problem of independence.…”

Comparing the empirical powers of several independence tests in generalized FGM family

“…Two other possible rank tests of independence are locally most powerful rank test and a powerful nonparametric test based on the Cramér-von Mises statistic. We also evaluate the empirical power of the class of distribution-free tests proposed by Kochar and Gupta (1987) based on the asymptotic distribution of a U-statistic and the test statistic proposed by Güven and Kotz (2008) in generalized Farlie-Gumbel-Morgenstern distribution. Tests of independence are also compared for sample sizes n = 20, 30, 50, empirically.…”