Buchumschlag

Projection-type estimation for varying coefficient regression models

In this paper we introduce new estimators of the coefficient functions in the varying coefficient regression model. The proposed estimators are obtained by projecting the vector of the full-dimensional kernel-weighted local polynomial estimators of the coefficient functions onto a Hilbert space with...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Lee, Young K. (VerfasserIn) , Mammen, Enno (VerfasserIn) , Park, Byeong U. (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 20 January 2012
In: Bernoulli
Year: 2012, Jahrgang: 18, Heft: 1, Pages: 177-205
ISSN:1573-9759
DOI:10.3150/10-BEJ331
Online-Zugang:Verlag, Volltext: http://dx.doi.org/10.3150/10-BEJ331
Verlag, Volltext: https://projecteuclid.org/euclid.bj/1327068622
Verlag, Volltext: https://projecteuclid.org/download/pdfview_1/euclid.bj/1327068622
Volltext
Verfasserangaben:Young K. Lee, Enno Mammen, Byeong U. Park
Beschreibung
Zusammenfassung:In this paper we introduce new estimators of the coefficient functions in the varying coefficient regression model. The proposed estimators are obtained by projecting the vector of the full-dimensional kernel-weighted local polynomial estimators of the coefficient functions onto a Hilbert space with a suitable norm. We provide a backfitting algorithm to compute the estimators. We show that the algorithm converges at a geometric rate under weak conditions. We derive the asymptotic distributions of the estimators and show that the estimators have the oracle properties. This is done for the general order of local polynomial fitting and for the estimation of the derivatives of the coefficient functions, as well as the coefficient functions themselves. The estimators turn out to have several theoretical and numerical advantages over the marginal integration estimators studied by Yang, Park, Xue and Härdle [J. Amer. Statist. Assoc. 101 (2006) 1212-1227].
Beschreibung:Gesehen am 31.01.2018
Beschreibung:Online Resource
ISSN:1573-9759
DOI:10.3150/10-BEJ331

Ähnliche Einträge