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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| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
De Gruyter
2019-09-01
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| coleção: | Dependence Modeling |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1515/demo-2019-0016 |
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