We introduce new rank tests for testing independence. The new testing procedures are sensitive not only for grade linear correlation, but also for grade correlations of higher-order polynomials. The number of polynomials involved is determined by the data. Model selection is combined with application of the score test in the selected model. Whereas well-known tests as Spearman's test or Hoeffding's test may completely break down for alternatives that are dependent but have low grade linear correlation, the new tests have greater power stability. Monte Carlo results clearly show this behavior. Theoretical support is obtained by proving consistency of the new tests
AbstractMultivariate generalizations of Bhuchongkul's bivariate rank statistics [Ann. Math. Statist....
We consider the problem of testing for multivariate independence in independent component (IC) mode...
A new approach is described for improving statistical tests of independence between two categorical ...
There is a lot of interest in positive dependence going beyond linear correlation. In this paper thr...
Rank correlations have found many innovative applications in the last decade. In particular,suitable...
Thesis (Ph.D.)--University of Washington, 2021Testing independence is a fundamental statistical prob...
New rank scores test statistics are proposed for testing whether two random vectors are independent....
Dependence measures and tests for independence have recently attracted a lot of attention, because t...
In testing independence of two random variables based on rank statistics, several rank statistics su...
A class of nonparametric tests based on the third quad-rant layer ranks has recently been studied by...
Blest (2000, Aust. N. Z. J. Stat. 42, 101-111) proposed a new measure of rank correlation that is se...
Dependence measures and tests for independence have recently attracted a lot of attention, because t...
<div><p>Dependence measures and tests for independence have recently attracted a lot of attention, b...
In the first part of our research, we propose a new interpoint-ranking sign covariance measure for ...
Defining multivariate generalizations of the classical univariate ranks has been a long-standing ope...
AbstractMultivariate generalizations of Bhuchongkul's bivariate rank statistics [Ann. Math. Statist....
We consider the problem of testing for multivariate independence in independent component (IC) mode...
A new approach is described for improving statistical tests of independence between two categorical ...
There is a lot of interest in positive dependence going beyond linear correlation. In this paper thr...
Rank correlations have found many innovative applications in the last decade. In particular,suitable...
Thesis (Ph.D.)--University of Washington, 2021Testing independence is a fundamental statistical prob...
New rank scores test statistics are proposed for testing whether two random vectors are independent....
Dependence measures and tests for independence have recently attracted a lot of attention, because t...
In testing independence of two random variables based on rank statistics, several rank statistics su...
A class of nonparametric tests based on the third quad-rant layer ranks has recently been studied by...
Blest (2000, Aust. N. Z. J. Stat. 42, 101-111) proposed a new measure of rank correlation that is se...
Dependence measures and tests for independence have recently attracted a lot of attention, because t...
<div><p>Dependence measures and tests for independence have recently attracted a lot of attention, b...
In the first part of our research, we propose a new interpoint-ranking sign covariance measure for ...
Defining multivariate generalizations of the classical univariate ranks has been a long-standing ope...
AbstractMultivariate generalizations of Bhuchongkul's bivariate rank statistics [Ann. Math. Statist....
We consider the problem of testing for multivariate independence in independent component (IC) mode...
A new approach is described for improving statistical tests of independence between two categorical ...
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