A robust regression methodology via M-estimation
| Autor: | Tao Yang, Colin M. Gallagher, Christopher S. McMahan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Estimation 021103 operations research Distribution (number theory) 0211 other engineering and technologies Hardware_PERFORMANCEANDRELIABILITY 02 engineering and technology 01 natural sciences Quantile regression Robust regression 010104 statistics & probability Skewness Statistics Random error Linear regression Hardware_INTEGRATEDCIRCUITS 0101 mathematics Mathematics |
| Zdroj: | Communications in Statistics - Theory and Methods. 48:1092-1107 |
| ISSN: | 1532-415X 0361-0926 |
| DOI: | 10.1080/03610926.2018.1423698 |
| Popis: | A robust regression methodology is proposed via M-estimation. The approach adapts to the tail behavior and skewness of the distribution of the random error terms, providing for a reliable analysis under a broad class of distributions. This is accomplished by allowing the objective function, used to determine the regression parameter estimates, to be selected in a data driven manner. The asymptotic properties of the proposed estimator are established and a numerical algorithm is provided to implement the methodology. The finite sample performance of the proposed approach is exhibited through simulation and the approach was used to analyze two motivating datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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