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A Simple Diagnostic Method of Outlier Detection for Stationary Gaussian Time Series / Yuzhi Cai; Davies, Neville

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

Swansea University Author: Yuzhi, Cai

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...

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Published in: Journal of Applied Statistics
Published: 2003
URI: https://cronfa.swan.ac.uk/Record/cronfa15298
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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.
Keywords: diagnostic method, additive outliers, innovation outliers, simulation, Gaussian time series.
College: School of Management
Start Page: 205
End Page: 223