A KERNEL-TYPE ESTIMATOR OF A QUANTILE FUNCTION UNDER RANDOMLY TRUNCATED DATA* * Zhou's research was partially supported by the NNSF of China (10471140, 10571169); Wu's research was partially supported by NNSF of China (0571170)

Yong Zhou; Guofu Wu; Daoji Li

doi:10.1016/S0252-9602(06)60084-2

A KERNEL-TYPE ESTIMATOR OF A QUANTILE FUNCTION UNDER RANDOMLY TRUNCATED DATA^* * Zhou's research was partially supported by the NNSF of China (10471140, 10571169); Wu's research was partially supported by NNSF of China (0571170)

Yong Zhou, Guofu Wu, Daoji Li

CAS - Academy of Mathematics and System Sciences

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

4 Scopus citations

Abstract

A kernel-type estimator of the quantile function Q(p) = inf {t : F(t) ≥ p}, 0 ≤ p ≤ 1, is proposed based on the kernel smoother when the data are subjected to random truncation. The Bahadur-type representations of the kernel smooth estimator are established, and from Bahadur representations the authors can show that this estimator is strongly consistent, asymptotically normal, and weakly convergent.

Original language	English
Pages (from-to)	585-594
Number of pages	10
Journal	Acta Mathematica Scientia
Volume	26
Issue number	4
DOIs	https://doi.org/10.1016/S0252-9602(06)60084-2
State	Published - 2006
Externally published	Yes

Keywords

Bahadur representation
Product-limits quantile function
Truncated data
kernel estimator

Access to Document

10.1016/S0252-9602(06)60084-2

Fingerprint

Dive into the research topics of 'A KERNEL-TYPE ESTIMATOR OF A QUANTILE FUNCTION UNDER RANDOMLY TRUNCATED DATA^* * Zhou's research was partially supported by the NNSF of China (10471140, 10571169); Wu's research was partially supported by NNSF of China (0571170)'. Together they form a unique fingerprint.

Cite this

@article{d889353da9fd423f93dca351dc4470bf,

title = "A KERNEL-TYPE ESTIMATOR OF A QUANTILE FUNCTION UNDER RANDOMLY TRUNCATED DATA* * Zhou's research was partially supported by the NNSF of China (10471140, 10571169); Wu's research was partially supported by NNSF of China (0571170)",

abstract = "A kernel-type estimator of the quantile function Q(p) = inf \{t : F(t) ≥ p\}, 0 ≤ p ≤ 1, is proposed based on the kernel smoother when the data are subjected to random truncation. The Bahadur-type representations of the kernel smooth estimator are established, and from Bahadur representations the authors can show that this estimator is strongly consistent, asymptotically normal, and weakly convergent.",

keywords = "Bahadur representation, Product-limits quantile function, Truncated data, kernel estimator",

author = "Yong Zhou and Guofu Wu and Daoji Li",

year = "2006",

doi = "10.1016/S0252-9602(06)60084-2",

language = "英语",

volume = "26",

pages = "585--594",

journal = "Acta Mathematica Scientia",

issn = "0252-9602",

publisher = "Springer",

number = "4",

}

A KERNEL-TYPE ESTIMATOR OF A QUANTILE FUNCTION UNDER RANDOMLY TRUNCATED DATA^* * Zhou's research was partially supported by the NNSF of China (10471140, 10571169); Wu's research was partially supported by NNSF of China (0571170). / Zhou, Yong; Wu, Guofu; Li, Daoji.
In: Acta Mathematica Scientia, Vol. 26, No. 4, 2006, p. 585-594.

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

TY - JOUR

T1 - A KERNEL-TYPE ESTIMATOR OF A QUANTILE FUNCTION UNDER RANDOMLY TRUNCATED DATA* * Zhou's research was partially supported by the NNSF of China (10471140, 10571169); Wu's research was partially supported by NNSF of China (0571170)

AU - Zhou, Yong

AU - Wu, Guofu

AU - Li, Daoji

PY - 2006

Y1 - 2006

N2 - A kernel-type estimator of the quantile function Q(p) = inf {t : F(t) ≥ p}, 0 ≤ p ≤ 1, is proposed based on the kernel smoother when the data are subjected to random truncation. The Bahadur-type representations of the kernel smooth estimator are established, and from Bahadur representations the authors can show that this estimator is strongly consistent, asymptotically normal, and weakly convergent.

AB - A kernel-type estimator of the quantile function Q(p) = inf {t : F(t) ≥ p}, 0 ≤ p ≤ 1, is proposed based on the kernel smoother when the data are subjected to random truncation. The Bahadur-type representations of the kernel smooth estimator are established, and from Bahadur representations the authors can show that this estimator is strongly consistent, asymptotically normal, and weakly convergent.

KW - Bahadur representation

KW - Product-limits quantile function

KW - Truncated data

KW - kernel estimator

UR - https://www.scopus.com/pages/publications/33750605927

U2 - 10.1016/S0252-9602(06)60084-2

DO - 10.1016/S0252-9602(06)60084-2

M3 - 文章

AN - SCOPUS:33750605927

SN - 0252-9602

VL - 26

SP - 585

EP - 594

JO - Acta Mathematica Scientia

JF - Acta Mathematica Scientia

IS - 4

ER -