Relative deficiency of quantile estimators for left truncated and right censored data

Mu Zhao; Fangfang Bai; Yong Zhou

doi:10.1016/j.spl.2011.06.016

Relative deficiency of quantile estimators for left truncated and right censored data

Mu Zhao
, Fangfang Bai^*
, Yong Zhou

^*Corresponding author for this work

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

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

The quantity deficiency which was proposed by Hodges and Lehmann (1970) is used to compare different statistical procedures. In this article, the deficiency of the sample quantile estimator with respect to the kernel quantile estimator for left truncated and right censored (LTRC) data in the sense of Hodges and Lehmann is considered. We also give the optimal bandwidth for the kernel quantile estimator. Monte Carlo studies are conducted to illustrate our results.

Original language	English
Pages (from-to)	1725-1732
Number of pages	8
Journal	Statistics and Probability Letters
Volume	81
Issue number	11
DOIs	https://doi.org/10.1016/j.spl.2011.06.016
State	Published - Nov 2011
Externally published	Yes

Keywords

Deficiency
Quantile estimator
Smoothed quantile estimator
Truncated and censored data

Access to Document

10.1016/j.spl.2011.06.016

Cite this

@article{4b9364b1407f4e28a249d1e11e28b5bd,

title = "Relative deficiency of quantile estimators for left truncated and right censored data",

abstract = "The quantity deficiency which was proposed by Hodges and Lehmann (1970) is used to compare different statistical procedures. In this article, the deficiency of the sample quantile estimator with respect to the kernel quantile estimator for left truncated and right censored (LTRC) data in the sense of Hodges and Lehmann is considered. We also give the optimal bandwidth for the kernel quantile estimator. Monte Carlo studies are conducted to illustrate our results.",

keywords = "Deficiency, Quantile estimator, Smoothed quantile estimator, Truncated and censored data",

author = "Mu Zhao and Fangfang Bai and Yong Zhou",

year = "2011",

month = nov,

doi = "10.1016/j.spl.2011.06.016",

language = "英语",

volume = "81",

pages = "1725--1732",

journal = "Statistics and Probability Letters",

issn = "0167-7152",

publisher = "Elsevier B.V.",

number = "11",

}

TY - JOUR

T1 - Relative deficiency of quantile estimators for left truncated and right censored data

AU - Zhao, Mu

AU - Bai, Fangfang

AU - Zhou, Yong

PY - 2011/11

Y1 - 2011/11

N2 - The quantity deficiency which was proposed by Hodges and Lehmann (1970) is used to compare different statistical procedures. In this article, the deficiency of the sample quantile estimator with respect to the kernel quantile estimator for left truncated and right censored (LTRC) data in the sense of Hodges and Lehmann is considered. We also give the optimal bandwidth for the kernel quantile estimator. Monte Carlo studies are conducted to illustrate our results.

AB - The quantity deficiency which was proposed by Hodges and Lehmann (1970) is used to compare different statistical procedures. In this article, the deficiency of the sample quantile estimator with respect to the kernel quantile estimator for left truncated and right censored (LTRC) data in the sense of Hodges and Lehmann is considered. We also give the optimal bandwidth for the kernel quantile estimator. Monte Carlo studies are conducted to illustrate our results.

KW - Deficiency

KW - Quantile estimator

KW - Smoothed quantile estimator

KW - Truncated and censored data

UR - https://www.scopus.com/pages/publications/79961118082

U2 - 10.1016/j.spl.2011.06.016

DO - 10.1016/j.spl.2011.06.016

M3 - 文章

AN - SCOPUS:79961118082

SN - 0167-7152

VL - 81

SP - 1725

EP - 1732

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 11

ER -

Relative deficiency of quantile estimators for left truncated and right censored data

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this