A model where the least trimmed squares estimator is maximum likelihood
<p>The least trimmed squares (LTS) estimator is a popular robust regression estimator. It finds a subsample of <em>h</em> ‘good’ observations among <em>n</em> observations and applies least squares on that subsampl...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Wiley
2023
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| _version_ | 1811139239487733760 |
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| author | Berenguer-Rico, V Johansen, S Nielsen, B |
| author_facet | Berenguer-Rico, V Johansen, S Nielsen, B |
| author_sort | Berenguer-Rico, V |
| collection | OXFORD |
| description | <p>The least trimmed squares (LTS) estimator is a popular robust regression estimator. It finds a subsample of <em>h</em> ‘good’ observations among <em>n</em> observations and applies least squares on that subsample. We formulate a model in which this estimator is maximum likelihood. The model has ‘outliers’ of a new type, where the outlying observations are drawn from a distribution with values outside the realized range of <em>h</em> ‘good’, normal observations. The LTS estimator is found to be <em>h</em><sup>1/2</sup> consistent and asymptotically standard normal in the location-scale case. Consistent estimation of <em>h</em> is discussed. The model differs from the commonly used <em>ϵ</em>-contamination models and opens the door for statistical discussion on contamination schemes, new methodological developments on tests for contamination as well as inferences based on the estimated good data.</p> |
| first_indexed | 2024-03-07T07:53:52Z |
| format | Journal article |
| id | oxford-uuid:039f0f75-72ac-4059-80e5-17d03fb19d25 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:02:56Z |
| publishDate | 2023 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:039f0f75-72ac-4059-80e5-17d03fb19d252024-05-15T10:12:01ZA model where the least trimmed squares estimator is maximum likelihoodJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:039f0f75-72ac-4059-80e5-17d03fb19d25EnglishSymplectic ElementsWiley2023Berenguer-Rico, VJohansen, SNielsen, B<p>The least trimmed squares (LTS) estimator is a popular robust regression estimator. It finds a subsample of <em>h</em> ‘good’ observations among <em>n</em> observations and applies least squares on that subsample. We formulate a model in which this estimator is maximum likelihood. The model has ‘outliers’ of a new type, where the outlying observations are drawn from a distribution with values outside the realized range of <em>h</em> ‘good’, normal observations. The LTS estimator is found to be <em>h</em><sup>1/2</sup> consistent and asymptotically standard normal in the location-scale case. Consistent estimation of <em>h</em> is discussed. The model differs from the commonly used <em>ϵ</em>-contamination models and opens the door for statistical discussion on contamination schemes, new methodological developments on tests for contamination as well as inferences based on the estimated good data.</p> |
| spellingShingle | Berenguer-Rico, V Johansen, S Nielsen, B A model where the least trimmed squares estimator is maximum likelihood |
| title | A model where the least trimmed squares estimator is maximum likelihood |
| title_full | A model where the least trimmed squares estimator is maximum likelihood |
| title_fullStr | A model where the least trimmed squares estimator is maximum likelihood |
| title_full_unstemmed | A model where the least trimmed squares estimator is maximum likelihood |
| title_short | A model where the least trimmed squares estimator is maximum likelihood |
| title_sort | model where the least trimmed squares estimator is maximum likelihood |
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