A class of distribution-free tests based on convex combination of two U-statistics is considered for testing independence against positive quadrant dependence. 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. It has been observed that some members of the class perform better than the existing tests in the literature. Unbiasedness and consistency of the proposed class of tests have been established
We consider the testing of mutual independence among all entries in a [Formula: see text]-dimensiona...
Thesis (Ph.D.)--University of Washington, 2021Testing independence is a fundamental statistical prob...
New test statistics are proposed for testing whether two random vectors are independent. Gieser and ...
A class of distribution-free tests based on convex combination of two U-statistics is considered for...
A new class of tests based on matched pairs for testing against positive quadrant dependence is prop...
A class of distribution-free tests has been proposed for testing independence against positive quadr...
A new class of tests based on convex combination of the two statistics is proposed. These are func...
We consider distributional free inference to test for positive quadrant dependence, i.e. for the pro...
We consider distributional free inference to test for positive quadrant dependence, that is, for the...
We develop an empirical likelihood (EL) approach to test independence of two univariate random varia...
Abstract. A popular approach for testing if two univariate random variables are statis-tically indep...
AbstractIn this paper, the set of all bivariate positive quadrant dependent distributions with fixed...
Three simple and explicit procedures for testing the independence of two multi-dimensional random va...
There is a lot of interest in positive dependence going beyond linear correlation. In this paper thr...
AbstractA class of distribution-free tests is proposed for the independence of two subsets of respon...
We consider the testing of mutual independence among all entries in a [Formula: see text]-dimensiona...
Thesis (Ph.D.)--University of Washington, 2021Testing independence is a fundamental statistical prob...
New test statistics are proposed for testing whether two random vectors are independent. Gieser and ...
A class of distribution-free tests based on convex combination of two U-statistics is considered for...
A new class of tests based on matched pairs for testing against positive quadrant dependence is prop...
A class of distribution-free tests has been proposed for testing independence against positive quadr...
A new class of tests based on convex combination of the two statistics is proposed. These are func...
We consider distributional free inference to test for positive quadrant dependence, i.e. for the pro...
We consider distributional free inference to test for positive quadrant dependence, that is, for the...
We develop an empirical likelihood (EL) approach to test independence of two univariate random varia...
Abstract. A popular approach for testing if two univariate random variables are statis-tically indep...
AbstractIn this paper, the set of all bivariate positive quadrant dependent distributions with fixed...
Three simple and explicit procedures for testing the independence of two multi-dimensional random va...
There is a lot of interest in positive dependence going beyond linear correlation. In this paper thr...
AbstractA class of distribution-free tests is proposed for the independence of two subsets of respon...
We consider the testing of mutual independence among all entries in a [Formula: see text]-dimensiona...
Thesis (Ph.D.)--University of Washington, 2021Testing independence is a fundamental statistical prob...
New test statistics are proposed for testing whether two random vectors are independent. Gieser and ...