Asymptotic theory of outlier detection algorithms for linear time series regression models: Rejoinder
Outlier detection algorithms are intimately connected with robust statistics that down-weight some observations to zero. We define a number of outlier detection algorithms related to the Huber-skip and least trimmed squares estimators, including the one-step Huber-skip estimator and the forward sear...
| Main Authors: | , |
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| Format: | Journal article |
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Wiley
2016
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| _version_ | 1826268891125383168 |
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| author | Nielsen, B Johansen, S |
| author_facet | Nielsen, B Johansen, S |
| author_sort | Nielsen, B |
| collection | OXFORD |
| description | Outlier detection algorithms are intimately connected with robust statistics that down-weight some observations to zero. We define a number of outlier detection algorithms related to the Huber-skip and least trimmed squares estimators, including the one-step Huber-skip estimator and the forward search. Next, we review a recently developed asymptotic theory of these. Finally, we analyse the gauge, the fraction of wrongly detected outliers, for a number of outlier detection algorithms and establish an asymptotic normal and a Poisson theory for the gauge. |
| first_indexed | 2024-03-06T21:16:31Z |
| format | Journal article |
| id | oxford-uuid:3ff6fbe4-7687-4ad6-af01-ca2817ec7e40 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T21:16:31Z |
| publishDate | 2016 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:3ff6fbe4-7687-4ad6-af01-ca2817ec7e402022-03-26T14:35:09ZAsymptotic theory of outlier detection algorithms for linear time series regression models: RejoinderJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3ff6fbe4-7687-4ad6-af01-ca2817ec7e40Symplectic Elements at OxfordWiley2016Nielsen, BJohansen, SOutlier detection algorithms are intimately connected with robust statistics that down-weight some observations to zero. We define a number of outlier detection algorithms related to the Huber-skip and least trimmed squares estimators, including the one-step Huber-skip estimator and the forward search. Next, we review a recently developed asymptotic theory of these. Finally, we analyse the gauge, the fraction of wrongly detected outliers, for a number of outlier detection algorithms and establish an asymptotic normal and a Poisson theory for the gauge. |
| spellingShingle | Nielsen, B Johansen, S Asymptotic theory of outlier detection algorithms for linear time series regression models: Rejoinder |
| title | Asymptotic theory of outlier detection algorithms for linear time series regression models: Rejoinder |
| title_full | Asymptotic theory of outlier detection algorithms for linear time series regression models: Rejoinder |
| title_fullStr | Asymptotic theory of outlier detection algorithms for linear time series regression models: Rejoinder |
| title_full_unstemmed | Asymptotic theory of outlier detection algorithms for linear time series regression models: Rejoinder |
| title_short | Asymptotic theory of outlier detection algorithms for linear time series regression models: Rejoinder |
| title_sort | asymptotic theory of outlier detection algorithms for linear time series regression models rejoinder |
| work_keys_str_mv | AT nielsenb asymptotictheoryofoutlierdetectionalgorithmsforlineartimeseriesregressionmodelsrejoinder AT johansens asymptotictheoryofoutlierdetectionalgorithmsforlineartimeseriesregressionmodelsrejoinder |