A model where the least trimmed squares estimator is maximum likelihood
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
The least trimmed squares (LTS) estimator is a popular robust regression estimator. It finds a subsample of h ‘good’ observations among n 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 h ‘good’, normal observations. The LTS estimator is found to be h 1/2 consistent and asymptotically standard normal in the location-scale case. Consistent estimation of h is discussed. The model differs from the commonly used E-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.
Originalsprog | Engelsk |
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Tidsskrift | Journal of The Royal Statistical Society Series B-statistical Methodology |
Vol/bind | 85 |
Udgave nummer | 3 |
Sider (fra-til) | 886-912 |
ISSN | 1369-7412 |
DOI | |
Status | Udgivet - 2023 |
ID: 355222550