ℒ1-deficiency of the sample quantile estimator with respect to a kernel quantile estimator

Mu Zhao; Hongmei Jiang; Yong Zhou

doi:10.1016/j.spl.2013.06.035

_ℒ1-deficiency of the sample quantile estimator with respect to a kernel quantile estimator

Mu Zhao
, Hongmei Jiang^*
, Yong Zhou

^*此作品的通讯作者

Northwestern University
Anhui University
CAS - Academy of Mathematics and System Sciences
Shanghai University of Finance and Economics

科研成果: 期刊稿件 › 文章 › 同行评审

1 引用（Scopus）

摘要

The performance of the sample quantile estimator versus a kernel quantile estimator under the criterion of mean integrated absolute error (MIAE) for randomly right-censored data is considered in this paper. We show that the so called _ℒ1-deficiency of the sample quantile estimator with respect to the kernel quantile estimator is convergent to infinity. The optimal bandwidth in the sense of MIAE is obtained. Some simulation studies and one real data analysis are used to illustrate the results.

源语言	英语
页（从-至）	2399-2406
页数	8
期刊	Statistics and Probability Letters
卷	83
期	10
DOI	https://doi.org/10.1016/j.spl.2013.06.035
出版状态	已出版 - 10月 2013
已对外发布	是

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10.1016/j.spl.2013.06.035

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abstract = "The performance of the sample quantile estimator versus a kernel quantile estimator under the criterion of mean integrated absolute error (MIAE) for randomly right-censored data is considered in this paper. We show that the so called ℒ1-deficiency of the sample quantile estimator with respect to the kernel quantile estimator is convergent to infinity. The optimal bandwidth in the sense of MIAE is obtained. Some simulation studies and one real data analysis are used to illustrate the results.",

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AU - Jiang, Hongmei

AU - Zhou, Yong

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N2 - The performance of the sample quantile estimator versus a kernel quantile estimator under the criterion of mean integrated absolute error (MIAE) for randomly right-censored data is considered in this paper. We show that the so called ℒ1-deficiency of the sample quantile estimator with respect to the kernel quantile estimator is convergent to infinity. The optimal bandwidth in the sense of MIAE is obtained. Some simulation studies and one real data analysis are used to illustrate the results.

AB - The performance of the sample quantile estimator versus a kernel quantile estimator under the criterion of mean integrated absolute error (MIAE) for randomly right-censored data is considered in this paper. We show that the so called ℒ1-deficiency of the sample quantile estimator with respect to the kernel quantile estimator is convergent to infinity. The optimal bandwidth in the sense of MIAE is obtained. Some simulation studies and one real data analysis are used to illustrate the results.

KW - -deficiency

KW - Kernel quantile estimator

KW - Optimal bandwidth

KW - Sample quantile estimator

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JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 10

ER -

ℒ1-deficiency of the sample quantile estimator with respect to a kernel quantile estimator

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_ℒ1-deficiency of the sample quantile estimator with respect to a kernel quantile estimator