Arbeitspapier
Oracally efficient two-step estimation of generalized additive model
Generalized additive models (GAM) are multivariate nonparametric regressions for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions. Our results are for weakly dependent data and we prove oracle efficiency. The SBK techniques is both computational expedient and theoretically reliable, thus usable for analyzing high-dimensional time series. Inference can be made on component functions based on asymptotic normality. Simulation evidence strongly corroborates with the asymptotic theory.
- Sprache
-
Englisch
- Erschienen in
-
Series: SFB 649 Discussion Paper ; No. 2011-016
- Klassifikation
-
Wirtschaft
- Thema
-
bandwidths
B spline
knots
link function
mixing
Nadaraya-Watson estimator
Multivariate Analyse
Nichtparametrisches Verfahren
Regression
Schätztheorie
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Liu, Rong
Yang, Lijian
Härdle, Wolfgang Karl
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (wo)
-
Berlin
- (wann)
-
2011
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Liu, Rong
- Yang, Lijian
- Härdle, Wolfgang Karl
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Entstanden
- 2011
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