Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter
This paper develops a robust profile estimation method for the parametric and nonparametric components of a single-index model when the errors have a strongly unimodal density with unknown nuisance parameter. We derive consistency results for the link function estimators as well as consistency and a...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_00203157_v_n_p_Agostinelli |
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todo:paper_00203157_v_n_p_Agostinelli2023-10-03T14:17:33Z Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter Agostinelli, C. Bianco, A.M. Boente, G. Fisher consistency Kernel weights Local polynomials Robustness Single-index models Errors Robustness (control systems) Asymptotic distributions Fisher consistency K fold cross validations Kernel weight Local polynomials Robustness properties Single index models Smoothing parameter Parameter estimation This paper develops a robust profile estimation method for the parametric and nonparametric components of a single-index model when the errors have a strongly unimodal density with unknown nuisance parameter. We derive consistency results for the link function estimators as well as consistency and asymptotic distribution results for the single-index parameter estimators. Under a log-Gamma model, the sensitivity to anomalous observations is studied using the empirical influence curve. We also discuss a robust K-fold cross-validation procedure to select the smoothing parameters. A numerical study carried on with errors following a log-Gamma model and for contaminated schemes shows the good robustness properties of the proposed estimators and the advantages of considering a robust approach instead of the classical one. A real data set illustrates the use of our proposal. © 2019, The Institute of Statistical Mathematics, Tokyo. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00203157_v_n_p_Agostinelli |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Fisher consistency Kernel weights Local polynomials Robustness Single-index models Errors Robustness (control systems) Asymptotic distributions Fisher consistency K fold cross validations Kernel weight Local polynomials Robustness properties Single index models Smoothing parameter Parameter estimation |
| spellingShingle |
Fisher consistency Kernel weights Local polynomials Robustness Single-index models Errors Robustness (control systems) Asymptotic distributions Fisher consistency K fold cross validations Kernel weight Local polynomials Robustness properties Single index models Smoothing parameter Parameter estimation Agostinelli, C. Bianco, A.M. Boente, G. Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter |
| topic_facet |
Fisher consistency Kernel weights Local polynomials Robustness Single-index models Errors Robustness (control systems) Asymptotic distributions Fisher consistency K fold cross validations Kernel weight Local polynomials Robustness properties Single index models Smoothing parameter Parameter estimation |
| description |
This paper develops a robust profile estimation method for the parametric and nonparametric components of a single-index model when the errors have a strongly unimodal density with unknown nuisance parameter. We derive consistency results for the link function estimators as well as consistency and asymptotic distribution results for the single-index parameter estimators. Under a log-Gamma model, the sensitivity to anomalous observations is studied using the empirical influence curve. We also discuss a robust K-fold cross-validation procedure to select the smoothing parameters. A numerical study carried on with errors following a log-Gamma model and for contaminated schemes shows the good robustness properties of the proposed estimators and the advantages of considering a robust approach instead of the classical one. A real data set illustrates the use of our proposal. © 2019, The Institute of Statistical Mathematics, Tokyo. |
| format |
JOUR |
| author |
Agostinelli, C. Bianco, A.M. Boente, G. |
| author_facet |
Agostinelli, C. Bianco, A.M. Boente, G. |
| author_sort |
Agostinelli, C. |
| title |
Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter |
| title_short |
Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter |
| title_full |
Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter |
| title_fullStr |
Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter |
| title_full_unstemmed |
Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter |
| title_sort |
robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter |
| url |
http://hdl.handle.net/20.500.12110/paper_00203157_v_n_p_Agostinelli |
| work_keys_str_mv |
AT agostinellic robustestimationinsingleindexmodelswhentheerrorshaveaunimodaldensitywithunknownnuisanceparameter AT biancoam robustestimationinsingleindexmodelswhentheerrorshaveaunimodaldensitywithunknownnuisanceparameter AT boenteg robustestimationinsingleindexmodelswhentheerrorshaveaunimodaldensitywithunknownnuisanceparameter |
| _version_ |
1807316330795237376 |