In this paper an asymptotic distribution is obtained for the maximaldeviation between the kernel quantile density estimator and the quantile density when the data aresubject to random left truncation and right censors...In this paper an asymptotic distribution is obtained for the maximaldeviation between the kernel quantile density estimator and the quantile density when the data aresubject to random left truncation and right censorship. Based on this result we propose a fullysequential procedure for constructing a fixed-width confidence band for the quantile density on afinite interval and show that the procedure has the desired coverage probability asymptotically asthe width of the band approaches zero.展开更多
In this article the authors establish the Bahadur type representations for the kernel quantileestimator and the kernel estimator of the derivatives of the quantile function on the basis of lefttruncated and right cens...In this article the authors establish the Bahadur type representations for the kernel quantileestimator and the kernel estimator of the derivatives of the quantile function on the basis of lefttruncated and right censored data. Under suitable conditions, with probability one, the exactconvergence rate of the remainder term in the representations is obtained. As a by-product, theLIL, the asymptotic normality for those kernel estimators are derived.展开更多
基金Supported by the National Natural Science Foundation of China (No.10471140)
文摘In this paper an asymptotic distribution is obtained for the maximaldeviation between the kernel quantile density estimator and the quantile density when the data aresubject to random left truncation and right censorship. Based on this result we propose a fullysequential procedure for constructing a fixed-width confidence band for the quantile density on afinite interval and show that the procedure has the desired coverage probability asymptotically asthe width of the band approaches zero.
文摘In this article the authors establish the Bahadur type representations for the kernel quantileestimator and the kernel estimator of the derivatives of the quantile function on the basis of lefttruncated and right censored data. Under suitable conditions, with probability one, the exactconvergence rate of the remainder term in the representations is obtained. As a by-product, theLIL, the asymptotic normality for those kernel estimators are derived.
