A class of nonparametric mode estimators

Standard

A class of nonparametric mode estimators. / Ruzankin, Pavel S.

In: Communications in Statistics: Simulation and Computation, Vol. 51, No. 6, 2022, p. 3291-3304.

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

Harvard

Ruzankin, PS 2022, 'A class of nonparametric mode estimators', Communications in Statistics: Simulation and Computation, vol. 51, no. 6, pp. 3291-3304. https://doi.org/10.1080/03610918.2019.1711410

APA

Ruzankin, P. S. (2022). A class of nonparametric mode estimators. Communications in Statistics: Simulation and Computation, 51(6), 3291-3304. https://doi.org/10.1080/03610918.2019.1711410

Vancouver

Ruzankin PS. A class of nonparametric mode estimators. Communications in Statistics: Simulation and Computation. 2022;51(6):3291-3304. doi: 10.1080/03610918.2019.1711410

Author

Ruzankin, Pavel S. / A class of nonparametric mode estimators. In: Communications in Statistics: Simulation and Computation. 2022 ; Vol. 51, No. 6. pp. 3291-3304.

BibTeX

@article{612b8b6a08e244048ec2c7e9ae517654,

title = "A class of nonparametric mode estimators",

abstract = "A class of nonparametric mode estimators is proposed. While the widely applied half sample mode estimators use the diameter of a set as the “measure of concentration”, the proposed estimators use for it some types of “variance measures”. In some cases, the new estimators perform better than half sample mode and half range mode estimators. Strong consistency is proved for an estimator from the class.",

keywords = "Fraction-of-sample mode, Half sample mode, Nonparametric mode estimator, ROBUST ESTIMATORS, DENSITY-FUNCTION, MULTIVARIATE, CONVERGENCE, EFFICIENT ESTIMATION",

author = "Ruzankin, {Pavel S.}",

note = "Publisher Copyright: {\textcopyright} 2020 Taylor & Francis Group, LLC.",

year = "2022",

doi = "10.1080/03610918.2019.1711410",

language = "English",

volume = "51",

pages = "3291--3304",

journal = "Communications in Statistics Part B: Simulation and Computation",

issn = "0361-0918",

publisher = "Taylor and Francis Ltd.",

number = "6",

}

RIS

TY - JOUR

T1 - A class of nonparametric mode estimators

AU - Ruzankin, Pavel S.

PY - 2022

Y1 - 2022

N2 - A class of nonparametric mode estimators is proposed. While the widely applied half sample mode estimators use the diameter of a set as the “measure of concentration”, the proposed estimators use for it some types of “variance measures”. In some cases, the new estimators perform better than half sample mode and half range mode estimators. Strong consistency is proved for an estimator from the class.

AB - A class of nonparametric mode estimators is proposed. While the widely applied half sample mode estimators use the diameter of a set as the “measure of concentration”, the proposed estimators use for it some types of “variance measures”. In some cases, the new estimators perform better than half sample mode and half range mode estimators. Strong consistency is proved for an estimator from the class.

KW - Fraction-of-sample mode

KW - Half sample mode

KW - Nonparametric mode estimator

KW - ROBUST ESTIMATORS

KW - DENSITY-FUNCTION

KW - MULTIVARIATE

KW - CONVERGENCE

KW - EFFICIENT ESTIMATION

UR - http://www.scopus.com/inward/record.url?scp=85078414841&partnerID=8YFLogxK

U2 - 10.1080/03610918.2019.1711410

DO - 10.1080/03610918.2019.1711410

M3 - Article

AN - SCOPUS:85078414841

VL - 51

SP - 3291

EP - 3304

JO - Communications in Statistics Part B: Simulation and Computation

JF - Communications in Statistics Part B: Simulation and Computation

SN - 0361-0918

IS - 6

ER -

ID: 23258315