On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior
We study nonparametric estimators of conditional Kendall’s tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic pointwise and uniform bounds, that hold with high probabilities. We provide “direct proofs” of the consistency and the asymptotic law o...
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De Gruyter
2019-09-01
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| Series: | Dependence Modeling |
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| Online Access: | https://doi.org/10.1515/demo-2019-0016 |
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doaj-5d0dfbb72b094cda86aa0738b066c88f2021-10-02T17:48:35ZengDe GruyterDependence Modeling2300-22982019-09-017129232110.1515/demo-2019-0016demo-2019-0016On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behaviorDerumigny Alexis0Fermanian Jean-David1CREST-ENSAE and University of Twente, 5 Drienerlolaan, 7522 NB Enschede, NetherlandsCREST-ENSAE, 5, avenue Henry Le Chatelier, 91764Palaiseau cedex, FranceWe study nonparametric estimators of conditional Kendall’s tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic pointwise and uniform bounds, that hold with high probabilities. We provide “direct proofs” of the consistency and the asymptotic law of conditional Kendall’s tau. A simulation study evaluates the numerical performance of such nonparametric estimators. An application to the dependence between energy consumption and temperature conditionally to calendar days is finally provided.https://doi.org/10.1515/demo-2019-0016conditional dependence measureskernel smoothingconditional kendall’s tau62h2062g0562g0862g20 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Derumigny Alexis Fermanian Jean-David |
| spellingShingle |
Derumigny Alexis Fermanian Jean-David On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior Dependence Modeling conditional dependence measures kernel smoothing conditional kendall’s tau 62h20 62g05 62g08 62g20 |
| author_facet |
Derumigny Alexis Fermanian Jean-David |
| author_sort |
Derumigny Alexis |
| title |
On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior |
| title_short |
On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior |
| title_full |
On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior |
| title_fullStr |
On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior |
| title_full_unstemmed |
On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior |
| title_sort |
on kernel-based estimation of conditional kendall’s tau: finite-distance bounds and asymptotic behavior |
| publisher |
De Gruyter |
| series |
Dependence Modeling |
| issn |
2300-2298 |
| publishDate |
2019-09-01 |
| description |
We study nonparametric estimators of conditional Kendall’s tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic pointwise and uniform bounds, that hold with high probabilities. We provide “direct proofs” of the consistency and the asymptotic law of conditional Kendall’s tau. A simulation study evaluates the numerical performance of such nonparametric estimators. An application to the dependence between energy consumption and temperature conditionally to calendar days is finally provided. |
| topic |
conditional dependence measures kernel smoothing conditional kendall’s tau 62h20 62g05 62g08 62g20 |
| url |
https://doi.org/10.1515/demo-2019-0016 |
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AT derumignyalexis onkernelbasedestimationofconditionalkendallstaufinitedistanceboundsandasymptoticbehavior AT fermanianjeandavid onkernelbasedestimationofconditionalkendallstaufinitedistanceboundsandasymptoticbehavior |
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