Large Sample Behavior of the Least Trimmed Squares Estimator
The least trimmed squares (LTS) estimator is popular in location, regression, machine learning, and AI literature. Despite the empirical version of least trimmed squares (LTS) being repeatedly studied in the literature, the population version of the LTS has never been introduced and studied. The lac...
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MDPI AG
2024-11-01
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| Series: | Mathematics |
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| _version_ | 1846153081190875136 |
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| author | Yijun Zuo |
| author_facet | Yijun Zuo |
| author_sort | Yijun Zuo |
| collection | DOAJ |
| description | The least trimmed squares (LTS) estimator is popular in location, regression, machine learning, and AI literature. Despite the empirical version of least trimmed squares (LTS) being repeatedly studied in the literature, the population version of the LTS has never been introduced and studied. The lack of the population version hinders the study of the large sample properties of the LTS utilizing the empirical process theory. Novel properties of the objective function in both empirical and population settings of the LTS and other properties, are established for the first time in this article. The primary properties of the objective function facilitate the establishment of other original results, including the influence function and Fisher consistency. The strong consistency is established with the help of a generalized Glivenko–Cantelli Theorem over a class of functions for the first time. Differentiability and stochastic equicontinuity promote the establishment of asymptotic normality with a concise and novel approach. |
| format | Article |
| id | doaj-art-82eefb307f5b42de849d31056117195d |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-82eefb307f5b42de849d31056117195d2024-11-26T18:11:55ZengMDPI AGMathematics2227-73902024-11-011222358610.3390/math12223586Large Sample Behavior of the Least Trimmed Squares EstimatorYijun Zuo0Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USAThe least trimmed squares (LTS) estimator is popular in location, regression, machine learning, and AI literature. Despite the empirical version of least trimmed squares (LTS) being repeatedly studied in the literature, the population version of the LTS has never been introduced and studied. The lack of the population version hinders the study of the large sample properties of the LTS utilizing the empirical process theory. Novel properties of the objective function in both empirical and population settings of the LTS and other properties, are established for the first time in this article. The primary properties of the objective function facilitate the establishment of other original results, including the influence function and Fisher consistency. The strong consistency is established with the help of a generalized Glivenko–Cantelli Theorem over a class of functions for the first time. Differentiability and stochastic equicontinuity promote the establishment of asymptotic normality with a concise and novel approach.https://www.mdpi.com/2227-7390/12/22/3586trimmed squares of residualscontinuity and differentiability of objective functioninfluence functionFisher consistencyasymptotics |
| spellingShingle | Yijun Zuo Large Sample Behavior of the Least Trimmed Squares Estimator Mathematics trimmed squares of residuals continuity and differentiability of objective function influence function Fisher consistency asymptotics |
| title | Large Sample Behavior of the Least Trimmed Squares Estimator |
| title_full | Large Sample Behavior of the Least Trimmed Squares Estimator |
| title_fullStr | Large Sample Behavior of the Least Trimmed Squares Estimator |
| title_full_unstemmed | Large Sample Behavior of the Least Trimmed Squares Estimator |
| title_short | Large Sample Behavior of the Least Trimmed Squares Estimator |
| title_sort | large sample behavior of the least trimmed squares estimator |
| topic | trimmed squares of residuals continuity and differentiability of objective function influence function Fisher consistency asymptotics |
| url | https://www.mdpi.com/2227-7390/12/22/3586 |
| work_keys_str_mv | AT yijunzuo largesamplebehavioroftheleasttrimmedsquaresestimator |