Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/12010

Title: Pseudo-likelihood methodology for partitioned large and complex samples
Authors: Molenberghs, Geert
Verbeke, Geert
Iddi, Samuel
Issue Date: 2011
Citation: STATISTICS & PROBABILITY LETTERS, 81 (7). p. 892-901
Abstract: Large data sets, either coming from a large number of independent replications, or because of hierarchies in the data with large numbers of within-unit replication, may pose challenges to the data analyst up to the point of making conventional inferential methods, such as maximum likelihood, prohibitive. Based on general pseudo-likelihood concepts, we propose a method to partition such a set of data, analyze each partition member, and properly combine the inferences into a single one. It is shown that the method is fully efficient for independent partitions, while with dependent sub-samples efficiency is sometimes but not always equal to one. It is argued that, for important realistic settings, efficiency is often very high. Illustrative examples enhance insight in the method's operation, while real-data analysis underscores its power for practice. (C) 2011 Elsevier B.V. All rights reserved.
Notes: [Molenberghs, Geert; Verbeke, Geert] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Molenberghs, Geert; Verbeke, Geert; Iddi, Samuel] Katholieke Univ Leuven, Interuniv Inst Biostat & Stat Bioinformat, B-3000 Louvain, Belgium.
URI: http://hdl.handle.net/1942/12010
DOI: 10.1016/j.spl.2011.01.012
ISI #: 000291175200024
ISSN: 0167-7152
Category: A1
Type: Journal Contribution
Validation: ecoom, 2012
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version253.29 kBAdobe PDF

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