Gross-Error Sensitivies of GM and RA-Estimates

Martin, R. Douglas; Yohai, Vı́ctor J.

doi:10.1007/978-1-4615-7821-5_11

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…This requirement is the essence of the general replacement model (Martin & Yohai, 1984). This requirement is the essence of the general replacement model (Martin & Yohai, 1984).…”

Section: Motivationmentioning

confidence: 99%

Robust and Consistent Estimates of Autoregressive-Moving Average Parameters

Masarotto

1987

Biometrika

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Biometrika Trust is collaborating with JSTOR to digitize, preserve and extend access to Biometrika. SUMMARYWe aim at estimates of autoregressive-moving average parameters which are simultaneously consistent at the nominal Gaussian model and insensitive to outliers in the data. The estimates may be viewed as a robust version of maximum likelihood estimates of order r and, for purely autoregressive models, reduce to a special case of generalized M-estimates.Some key words: Autoregressive-moving average model; Generalized M-estimate; Maximum likelihood estimation of order r; Robust estimation.

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“…This requirement is the essence of the general replacement model (Martin & Yohai, 1984). This requirement is the essence of the general replacement model (Martin & Yohai, 1984).…”

Section: Motivationmentioning

confidence: 99%

Robust and Consistent Estimates of Autoregressive-Moving Average Parameters

Masarotto

1987

Biometrika

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“…In both models the quantity and the position of the outlier is deterministic. Thus another model is present as usually discussed in robust statistics, where each value has a certain probability of being an outlier (AO, IO-model, see Martin (1979), Martin & Yohai (1984)).…”

Section: Introductionmentioning

confidence: 99%

The Multiple Outlier Problem in Time Series Analysis

Schmid

1986

Australian Journal of Statistics

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Summary In this paper we consider the multiple outlier problem in time series analysis. The underlying undisturbed time series is assumed to be an autoregressive process. The location of the suspicious values is supposed to be known. We introduce conditional least squares estimators for the parameters. The estimates are shown to be strongly consistent. Using similar arguments as in the theory of linear models, we get a test statistic for the general linear hypothesis. Its asymptotic distribution is derived.

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Fault detection in ARX process

Kobi¹,

Ouladsine²,

Ragot³

Proceedings of IEEE Systems Man and Cybernetics Conference - SMC

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Gross-Error Sensitivies of GM and RA-Estimates

Cited by 3 publications

References 8 publications

Robust and Consistent Estimates of Autoregressive-Moving Average Parameters

Robust and Consistent Estimates of Autoregressive-Moving Average Parameters

The Multiple Outlier Problem in Time Series Analysis

Fault detection in ARX process

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