Articles liés à ADAPTIVE FREE-KNOT SPLINES AND INFERENCE: Free-knot...

ADAPTIVE FREE-KNOT SPLINES AND INFERENCE: Free-knot splines and adaptive model selection - Couverture souple

Miyata, Satoshi

 
9783639173451: ADAPTIVE FREE-KNOT SPLINES AND INFERENCE: Free-knot splines and adaptive model selection
Couverture souple
ISBN 10 : 3639173457 ISBN 13 : 9783639173451
Editeur : VDM Verlag, 2009

Synopsis

In this book, we propose a function estimation procedure using adaptive free-knot splines (AFKS) and associated algorithm for implementation in nonparametric regression. Knot selection is extremely important for estimating a function with complicated structure by spline functions. The AFKS allows variable knot numbers and replacements of knots at any locations and multiple knots at the same location. This flexibility leads to an adaptive spline estimator that adapts to any function with inhomogeneous smoothness, including a sharp kink or discontinuity, which substantially improves the representation power of splines. A new knot selection scheme is proposed, by utilizing an adaptive model selection criterion (AMSC) and an Evolutionary Algorithm (EA). The AMSC guards against the selection error in searching through a large candidate knot space, while the EA enables us to search the global minima of AMSC. The performance of the AFKS is examined via simulations, which demonstrates its superiority over the existing spline methods and wavelet estimators. This book would be useful for researchers who analyze real and complex data structures.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

In this book, we propose a function estimation procedure using adaptive free-knot splines (AFKS) and associated algorithm for implementation in nonparametric regression. Knot selection is extremely important for estimating a function with complicated structure by spline functions. The AFKS allows variable knot numbers and replacements of knots at any locations and multiple knots at the same location. This flexibility leads to an adaptive spline estimator that adapts to any function with inhomogeneous smoothness, including a sharp kink or discontinuity, which substantially improves the representation power of splines. A new knot selection scheme is proposed, by utilizing an adaptive model selection criterion (AMSC) and an Evolutionary Algorithm (EA). The AMSC guards against the selection error in searching through a large candidate knot space, while the EA enables us to search the global minima of AMSC. The performance of the AFKS is examined via simulations, which demonstrates its superiority over the existing spline methods and wavelet estimators. This book would be useful for researchers who analyze real and complex data structures.

Biographie de l'auteur

Satoshi Miyata, Ph. D.: Studied Statistics at Hitotsubashi University and the Ohio State University. Researcher at Japanese Foundation for Cancer Research, Tokyo, Japan. Lecturer at Tokyo University of Agriculture and Technology, Hitotsubashi University and National Graduate Institute for Policy Studies, Japan.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag
Date d'édition
2009
Langue
anglais
ISBN 10 
3639173457
ISBN 13 
9783639173451
Reliure
Broché
Nombre de pages
108
Coordonnées du fabricant
non disponible
Personne responsable
non disponible