www.uhasselt.be
DSpace

Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2055

Title: Kernel weighted influence measures
Authors: Hens, Niel
Aerts, Marc
Molenberghs, Geert
Thijs, Herbert
Verbeke, Geert
Issue Date: 2005
Publisher: ELSEVIER SCIENCE BV
Citation: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 48(3). p. 467-487
Abstract: To asses the sensitivity of conclusions to model choices in the context of selection models for non-random dropout, several methods have been developed. None of them are without limitations. A new method called kernel weighted influence is proposed. While global and local influence approaches look upon the influence of cases, this new method looks at the influence of types of observations. The basic idea is to combine the existing influence approaches with a non-parametric weighting scheme. The kernel weighted global influence offers a possible solution to the problem of masking, while the kernel weighted local influence can be seen as a tool to better understand the source of influence. (C) 2004 Elsevier B.V. All rights reserved.
Notes: Limburgs Univ Ctr, Ctr Stat, B-3590 Diepenbeek, Belgium. Katholieke Univ Leuven, Ctr Biostat, B-3000 Louvain, Belgium.Hens, N, Limburgs Univ Ctr, Ctr Stat, Univ Campus,Bldg D, B-3590 Diepenbeek, Belgium.niel.hens@luc.ac.be
URI: http://hdl.handle.net/1942/2055
DOI: 10.1016/j.csda.2004.02.010
ISI #: 000226475800003
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Validation: ecoom, 2006
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version1.73 MBAdobe PDF
Peer-reviewed author version1.88 MBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.