Testing for Multivariate Outliers in the Presence of Missing Data
β Scribed by W. A. Woodward<ORF RID="A1">; S. R. Sain<ORF RID="A1">; H. L. Gray<ORF RID="A1">; B. Zhao<ORF RID="A1">
- Publisher
- Springer
- Year
- 2002
- Tongue
- English
- Weight
- 187 KB
- Volume
- 159
- Category
- Article
- ISSN
- 0033-4533
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In this paper we investigate the properties of the Lagrange Multiplier [LM] test for autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) in the presence of additive outliers (AOs). We show analytically that both the asymptotic size and power are adversely aected if AOs
Rohlf (1975, Biometrics 31, 93-101) proposed a method of detecting outliers in multivariate data by testing the largest edge of the minimum spanning tree. It is shown here that tests against the gamma distribution are extremely liberal. Furthermore, results depend on the correlation structure of the