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  An Inference and Integration Approach for the Consolidation of Ranked Lists

Schimek, M. G., Mysickova, A., & Budinská, E. (2012). An Inference and Integration Approach for the Consolidation of Ranked Lists. Communications in Statistics - Simulation and Computation, 41(7), 1152-1166. doi:10.1080/03610918.2012.625843.

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 Creators:
Schimek, Michael G.1, Author
Mysickova, Alena2, Author              
Budinská, Eva3, Author
Affiliations:
1 Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria, ou_persistent22              
2Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, Berlin, Germany, ou_1433547              
3 Swiss Institute of Bioinformatics, Lausanne, Switzerlandand Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic, ou_persistent22              

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Free keywords: Cross-entropy Monte Carlo, Kendall's τ, Moderate deviation, Partial list, Random degeneration, Rank aggregation, Spearman's footrule, Top-k ranked list
 Abstract: In this article, we describe a new approach that combines the estimation of the lengths of highly conforming sublists with their stochastic aggregation, to deal with two or more rankings of the same set of objects. The goal is to obtain a much smaller set of informative common objects in a new rank order. The input lists can be of large or huge size, their rankings irregular and incomplete due to random and missing assignments. A moderate deviation-based inference procedure and a cross-entropy Monte Carlo technique are used to handle the combinatorial complexity of the task. Two alternative distance measures are considered that can accommodate truncated list information. Finally, the outlined approach is applied to simulated data that was motivated by microarray meta-analysis, an important field of application.

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Language(s): eng - English
 Dates: 2012-04-022012-07
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1080/03610918.2012.625843
 Degree: -

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Title: Communications in Statistics - Simulation and Computation
Source Genre: Journal
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Publ. Info: Taylor & Francis
Pages: - Volume / Issue: 41 (7) Sequence Number: - Start / End Page: 1152 - 1166 Identifier: ISSN: 0361-0918