Kernel estimators of mode under Psi-weak dependence
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwang, Eunju | - |
dc.contributor.author | Shin, Dong Wan | - |
dc.date.available | 2020-02-28T02:42:32Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2016-04 | - |
dc.identifier.issn | 0020-3157 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/8389 | - |
dc.description.abstract | Nonparametric kernel-type estimation is discussed for modes which maximize nonparametric kernel-type density estimators. The discussion is made under a weak dependence condition which unifies weak dependence conditions such as mixing, association, Gaussian sequences and Bernoulli shifts. Consistency and asymptotic normality are established for the mode estimator as well as for kernel estimators of density derivatives. The convergence rate of the mode estimator is given in terms of the bandwidth. An optimal bandwidth selection procedure is proposed for mode estimation. A Monte-Carlo experiment shows that the proposed bandwidth yields a substantially better mode estimator than the common bandwidths optimized for density estimation. Modes of log returns of Dow Jones index and foreign exchange rates of US Dollar relative to Euro are investigated in terms of asymmetry. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER HEIDELBERG | - |
dc.relation.isPartOf | ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | - |
dc.subject | MOMENT INEQUALITIES | - |
dc.subject | RANDOM-VARIABLES | - |
dc.subject | NONPARAMETRIC REGRESSION | - |
dc.subject | ASYMPTOTIC NORMALITY | - |
dc.subject | DENSITY-ESTIMATION | - |
dc.subject | CONVERGENCE RATE | - |
dc.subject | CONSISTENCY | - |
dc.subject | PROBABILITY | - |
dc.subject | DERIVATIVES | - |
dc.subject | SEQUENCES | - |
dc.title | Kernel estimators of mode under Psi-weak dependence | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000371237000004 | - |
dc.identifier.doi | 10.1007/s10463-014-0489-2 | - |
dc.identifier.bibliographicCitation | ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, v.68, no.2, pp.301 - 327 | - |
dc.identifier.scopusid | 2-s2.0-84959322582 | - |
dc.citation.endPage | 327 | - |
dc.citation.startPage | 301 | - |
dc.citation.title | ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | - |
dc.citation.volume | 68 | - |
dc.citation.number | 2 | - |
dc.contributor.affiliatedAuthor | Hwang, Eunju | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Weak dependence | - |
dc.subject.keywordAuthor | Kernel estimator | - |
dc.subject.keywordAuthor | Mode | - |
dc.subject.keywordAuthor | Consistency | - |
dc.subject.keywordAuthor | Asymptotic normality | - |
dc.subject.keywordAuthor | Bandwidth | - |
dc.subject.keywordAuthor | Asymmetry | - |
dc.subject.keywordPlus | MOMENT INEQUALITIES | - |
dc.subject.keywordPlus | RANDOM-VARIABLES | - |
dc.subject.keywordPlus | NONPARAMETRIC REGRESSION | - |
dc.subject.keywordPlus | ASYMPTOTIC NORMALITY | - |
dc.subject.keywordPlus | DENSITY-ESTIMATION | - |
dc.subject.keywordPlus | CONVERGENCE RATE | - |
dc.subject.keywordPlus | CONSISTENCY | - |
dc.subject.keywordPlus | PROBABILITY | - |
dc.subject.keywordPlus | DERIVATIVES | - |
dc.subject.keywordPlus | SEQUENCES | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.