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预调整的条件分布函数非参数估计AI 解读

Summary The paper deals with non-parametric estimation of a conditional distribution function. We suggest a method of preadjusting the original observations non-parametrically through location and scale, to reduce the bias of the estimator. We derive the asymptotic properties of the estimator proposed. A simulation study investigating the finite sample performances of the estimators discussed is provided and reveals the gain that can be achieved. It is also shown how the…

Copula 的改进核估计：弱收敛与拟合优度检验AI 解读

We reconsider the existing kernel estimators for a copula function, as proposed in Gijbels and Mielniczuk [Comm. Statist. Theory Methods 19 (1990) 445–464], Fermanian, Radulovič and Wegkamp [Bernoulli 10 (2004) 847–860] and Chen and Huang [Canad. J. Statist. 35 (2007) 265–282]. All of these estimators have as a drawback that they can suffer from a corner bias problem. A way to deal with this is to impose rather stringent conditions on the copula, outruling as such many…