Arbeitspapier
Smoothing with curvature constraints based on boosting techniques
In many applications it is known that the underlying smooth function is constrained to have a specific form. In the present paper, we propose an estimation method based on the regression spline approach, which allows to include concavity or convexity constraints in an appealing way. Instead of using linear or quadratic programming routines, we handle the required inequality constraints on basis coefficients by boosting techniques. Therefore, recently developed componentwise boosting methods for regression purposes are applied, which allow to control the restrictions in each iteration. The proposed approach is compared to several competitors in a simulation study. We also consider a real world data set.
- Sprache
-
Englisch
- Erschienen in
-
Series: Discussion Paper ; No. 467
- Thema
-
Shape constrained smoothing
Concavity
Regression splines
Boosting
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Leitenstorfer, Florian
Tutz, Gerhard
- Ereignis
-
Veröffentlichung
- (wer)
-
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
- (wo)
-
München
- (wann)
-
2006
- DOI
-
doi:10.5282/ubm/epub.1835
- Handle
- URN
-
urn:nbn:de:bvb:19-epub-1835-2
- Letzte Aktualisierung
-
20.09.2024, 08:21 MESZ
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
- Leitenstorfer, Florian
- Tutz, Gerhard
- Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
Entstanden
- 2006
Ähnliche Objekte (12)
Smoothing: Local Regression Techniques
Smoothing densities under shape constraints
Knot selection by boosting techniques
Smoothing techniques : with implementation in S
Smoothing singularities of Riemannian metrics while preserving lower curvature bounds
Application of Smoothing Techniques to Implied Volatility
Spatial smoothing techniques for the assessment of habitat suitability
Smoothing the Undersampled Carpal Bone Model with Small Volume and Large Curvature : A Feasibility Study
Website Boosting
Bumps in river profiles : uncertainty assessment and smoothing using quantile regression techniques
Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques
Boosting ridge regression
Smoothing: Local Regression Techniques
Smoothing densities under shape constraints
Knot selection by boosting techniques
Smoothing techniques : with implementation in S
Smoothing singularities of Riemannian metrics while preserving lower curvature bounds
Application of Smoothing Techniques to Implied Volatility
Spatial smoothing techniques for the assessment of habitat suitability
Smoothing the Undersampled Carpal Bone Model with Small Volume and Large Curvature : A Feasibility Study
Website Boosting
Bumps in river profiles : uncertainty assessment and smoothing using quantile regression techniques
Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques
Boosting ridge regression
Smoothing: Local Regression Techniques
Smoothing densities under shape constraints
Knot selection by boosting techniques
Smoothing techniques : with implementation in S
Smoothing singularities of Riemannian metrics while preserving lower curvature bounds
Application of Smoothing Techniques to Implied Volatility
Spatial smoothing techniques for the assessment of habitat suitability
Smoothing the Undersampled Carpal Bone Model with Small Volume and Large Curvature : A Feasibility Study
Website Boosting
Bumps in river profiles : uncertainty assessment and smoothing using quantile regression techniques
Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques