A New Type of LASSO Regression Model with Cauchy Noise
Many datasets have heavy-tailed behavior, and classical penalized models are not appropriate for them. To treat this problem, we propose a penalized regression that handles model selection and outliers issues simultaneously. We provide a LASSO regression for models with Cauchy distributed noises usi...
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| Veröffentlicht in: | Journal of agricultural, biological, and environmental statistics biological, and environmental statistics, 2024-06, Vol.29 (2), p.277-300 |
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| container_end_page | 300 |
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| container_issue | 2 |
| container_start_page | 277 |
| container_title | Journal of agricultural, biological, and environmental statistics |
| container_volume | 29 |
| creator | Ghatari, Amir Hossein Aminghafari, Mina Mohammadpour, Adel |
| description | Many datasets have heavy-tailed behavior, and classical penalized models are not appropriate for them. To treat this problem, we propose a penalized regression that handles model selection and outliers issues simultaneously. We provide a LASSO regression for models with Cauchy distributed noises using the negative log-likelihood loss function. To select the regularization parameter, we define
AIC
and
BIC
type criteria. We study the distribution of the regression coefficients estimator in the simulation experiments. In addition, simulation study and real datasets analysis confirm the superiority of the proposed method. |
| doi_str_mv | 10.1007/s13253-023-00583-w |
| format | Article |
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AIC
and
BIC
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AIC
and
BIC
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AIC
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BIC
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| issn | 1085-7117 1537-2693 |
| language | eng |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Agriculture Biostatistics data collection Datasets Health Sciences Mathematics and Statistics Medicine Monitoring/Environmental Analysis Outliers (statistics) Regression analysis Regression coefficients Regression models Regularization sounds Statistics Statistics for Life Sciences |
| title | A New Type of LASSO Regression Model with Cauchy Noise |
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