Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence
We consider automatic data‐driven density, regression and autoregression estimates, based on any random bandwidth selector h/T. We show that in a first‐order asymptotic approximation they behave as well as the related estimates obtained with the “optimal” bandwidth hT as long as hT/hT → 1 in probabi...
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todo:paper_03195724_v23_n4_p383_Boente2023-10-03T15:23:12Z Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence Boente, G. Fraiman, R. 62M10. autoregression models Data‐driven bandwidth selectors density estimation kernel estimates nonparametric regression Primary 62G05 secondary 62G20 α‐mixing processes We consider automatic data‐driven density, regression and autoregression estimates, based on any random bandwidth selector h/T. We show that in a first‐order asymptotic approximation they behave as well as the related estimates obtained with the “optimal” bandwidth hT as long as hT/hT → 1 in probability. The results are obtained for dependent observations; some of them are also new for independent observations. Copyright © 1995 Statistical Society of Canada JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03195724_v23_n4_p383_Boente |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
62M10. autoregression models Data‐driven bandwidth selectors density estimation kernel estimates nonparametric regression Primary 62G05 secondary 62G20 α‐mixing processes |
| spellingShingle |
62M10. autoregression models Data‐driven bandwidth selectors density estimation kernel estimates nonparametric regression Primary 62G05 secondary 62G20 α‐mixing processes Boente, G. Fraiman, R. Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence |
| topic_facet |
62M10. autoregression models Data‐driven bandwidth selectors density estimation kernel estimates nonparametric regression Primary 62G05 secondary 62G20 α‐mixing processes |
| description |
We consider automatic data‐driven density, regression and autoregression estimates, based on any random bandwidth selector h/T. We show that in a first‐order asymptotic approximation they behave as well as the related estimates obtained with the “optimal” bandwidth hT as long as hT/hT → 1 in probability. The results are obtained for dependent observations; some of them are also new for independent observations. Copyright © 1995 Statistical Society of Canada |
| format |
JOUR |
| author |
Boente, G. Fraiman, R. |
| author_facet |
Boente, G. Fraiman, R. |
| author_sort |
Boente, G. |
| title |
Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence |
| title_short |
Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence |
| title_full |
Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence |
| title_fullStr |
Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence |
| title_full_unstemmed |
Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence |
| title_sort |
asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence |
| url |
http://hdl.handle.net/20.500.12110/paper_03195724_v23_n4_p383_Boente |
| work_keys_str_mv |
AT boenteg asymptoticdistributionofdatadrivensmoothersindensityandregressionestimationunderdependence AT fraimanr asymptoticdistributionofdatadrivensmoothersindensityandregressionestimationunderdependence |
| _version_ |
1782029337766133760 |