No Cover Image

Journal article 1195 views

A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series

Yuzhi Cai Orcid Logo, Davies, Neville

Journal of Applied Statistics, Volume: 30, Pages: 205 - 223

Swansea University Author: Yuzhi Cai Orcid Logo

Abstract

In this paper we present a ``model free'' method of outlier detectionfor Gaussian time series by using the autocorrelation structure of thetime series. We also present a graphic diagnostic methodin order to distinguish an AO from IO. The test statistic fordetecting the outlierhas a $\chi^2...

Full description

Published in: Journal of Applied Statistics
Published: 2003
URI: https://cronfa.swan.ac.uk/Record/cronfa15298
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2013-08-22T01:57:38Z
last_indexed 2018-02-09T04:47:09Z
id cronfa15298
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2013-07-30T11:19:17.9979823</datestamp><bib-version>v2</bib-version><id>15298</id><entry>2013-07-30</entry><title>A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series</title><swanseaauthors><author><sid>eff7b8626ab4cc6428eef52516fda7d6</sid><ORCID>0000-0003-3509-9787</ORCID><firstname>Yuzhi</firstname><surname>Cai</surname><name>Yuzhi Cai</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2013-07-30</date><deptcode>BAF</deptcode><abstract>In this paper we present a ``model free'' method of outlier detectionfor Gaussian time series by using the autocorrelation structure of thetime series. We also present a graphic diagnostic methodin order to distinguish an AO from IO. The test statistic fordetecting the outlierhas a $\chi^2$ distribution with one degree of freedom. We show thatthis method works well when time series contain either one type of theoutliers or both additive andinnovation type outliers, and this method has the advantage that notime seriesmodel needs to be estimated from the data. Simulation evidence showsthat different types of outliers can be graphically distinguishedby using the techniques proposed.</abstract><type>Journal Article</type><journal>Journal of Applied Statistics</journal><volume>30</volume><journalNumber></journalNumber><paginationStart>205</paginationStart><paginationEnd>223</paginationEnd><publisher/><placeOfPublication/><issnPrint/><issnElectronic/><keywords>diagnostic method, additive outliers, innovation outliers, simulation, Gaussian time series.</keywords><publishedDay>30</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2003</publishedYear><publishedDate>2003-06-30</publishedDate><doi/><url/><notes/><college>COLLEGE NANME</college><department>Accounting and Finance</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BAF</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2013-07-30T11:19:17.9979823</lastEdited><Created>2013-07-30T11:09:03.8718617</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Accounting and Finance</level></path><authors><author><firstname>Yuzhi</firstname><surname>Cai</surname><orcid>0000-0003-3509-9787</orcid><order>1</order></author><author><firstname>Davies,</firstname><surname>Neville</surname><order>2</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2013-07-30T11:19:17.9979823 v2 15298 2013-07-30 A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2013-07-30 BAF In this paper we present a ``model free'' method of outlier detectionfor Gaussian time series by using the autocorrelation structure of thetime series. We also present a graphic diagnostic methodin order to distinguish an AO from IO. The test statistic fordetecting the outlierhas a $\chi^2$ distribution with one degree of freedom. We show thatthis method works well when time series contain either one type of theoutliers or both additive andinnovation type outliers, and this method has the advantage that notime seriesmodel needs to be estimated from the data. Simulation evidence showsthat different types of outliers can be graphically distinguishedby using the techniques proposed. Journal Article Journal of Applied Statistics 30 205 223 diagnostic method, additive outliers, innovation outliers, simulation, Gaussian time series. 30 6 2003 2003-06-30 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2013-07-30T11:19:17.9979823 2013-07-30T11:09:03.8718617 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1 Davies, Neville 2
title A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series
spellingShingle A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series
Yuzhi Cai
title_short A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series
title_full A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series
title_fullStr A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series
title_full_unstemmed A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series
title_sort A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
Davies, Neville
format Journal article
container_title Journal of Applied Statistics
container_volume 30
container_start_page 205
publishDate 2003
institution Swansea University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 0
active_str 0
description In this paper we present a ``model free'' method of outlier detectionfor Gaussian time series by using the autocorrelation structure of thetime series. We also present a graphic diagnostic methodin order to distinguish an AO from IO. The test statistic fordetecting the outlierhas a $\chi^2$ distribution with one degree of freedom. We show thatthis method works well when time series contain either one type of theoutliers or both additive andinnovation type outliers, and this method has the advantage that notime seriesmodel needs to be estimated from the data. Simulation evidence showsthat different types of outliers can be graphically distinguishedby using the techniques proposed.
published_date 2003-06-30T03:17:26Z
_version_ 1763750388525170688
score 11.036706

Similar Items