A new testing procedure uses critical surfaces on the quantile dependence function to detect local dependence while preserving the global significance level.
Dependence function for bivariate cdf's
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abstract
Measuring a strength of dependence of random variables is an important problem in statistical practice. In this paper, we propose a new function valued measure of dependence of two random variables. It allows one to study and visualize explicit dependence structure, both in some theoretical models and empirically, without prior model structure. This provides a comprehensive view of association structure and makes possible much detailed inference than based on standard numeric measures of association. We present theoretical properties of the new measure of dependence and discuss in detail estimation and application of copula-based variant of it. Some artificial and real data examples illustrate the behavior and practical utility of the measure and its estimator.
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UNVERDICTED 2representative citing papers
A novel rank-based estimator of the quantile dependence function with local acceptance regions allows visualization of dependence structure and supports a finite-sample valid independence test that performs well in power across many alternatives.
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The post-hoc test for local dependence
A new testing procedure uses critical surfaces on the quantile dependence function to detect local dependence while preserving the global significance level.
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Detecting dependence structure: visualization and inference
A novel rank-based estimator of the quantile dependence function with local acceptance regions allows visualization of dependence structure and supports a finite-sample valid independence test that performs well in power across many alternatives.