Arbeitspapier
Modifying the double smoothing bandwidth selector in nonparametric regression
In this paper a modified double smoothing bandwidth selector, ^h MDS , based on a new criterion, which combines the plug-in and the double smoothing ideas, is proposed. A self-complete iterative double smoothing rule (^h_IDS ) is introduced as a pilot method. The asymptotic properties of both ^h_IDS and ^h_MDS are investigated. It is shown that ^ h MDS performs asymptotically very well. Moreover, it is asymptotically negatively correlated with h ASE , the minimizer of the averaged squared error. The asymptotic performances of ^h_MDS and of the iterative plug-in method, ^h_IPL (Gasser et al., 1991) are compared. A comparative simulation study is carried out to show the practical perfor- mance of ^h_MDS and related methods. It is shown that ^h_MDS seems to be the best in the practice. Finite sample negative correlations between the chosen bandwidth selectors and h ASE are also studied.
- Sprache
-
Englisch
- Erschienen in
-
Series: CoFE Discussion Paper ; No. 00/37
- Klassifikation
-
Wirtschaft
- Thema
-
Regression
Nichtparametrisches Verfahren
Schätztheorie
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Beran, Jan
Feng, Yuanhua
Heiler, Siegfried
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Konstanz, Center of Finance and Econometrics (CoFE)
- (wo)
-
Konstanz
- (wann)
-
2000
- Handle
- URN
-
urn:nbn:de:bsz:352-opus-6152
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Beran, Jan
- Feng, Yuanhua
- Heiler, Siegfried
- University of Konstanz, Center of Finance and Econometrics (CoFE)
Entstanden
- 2000
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