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Editorial

Editorial Journal of Classification 35:1-4 (2018) DOI: 10.1007/s00357-018-9254-1 The first paper in this issue of the Journal of Classification by Fionn Murtagh, Michael Orlov, and Boris Mirkin addresses the ever tricky issue of evaluating the quality of research produced by individuals. The approach by Murtagh and coauthors avoids many of the issues that are leveled against common metrics of productivity (like impact factor for instance, see Brumback, 2009; Brischoux and Cook, 2009; Rossner, Epps, and Hill, 2007) by taking a more holistic approach to assessing a researcher’s contributions. Personally, I believe that this is one of first efforts of what promises to be many forays into taking a broad approach at tackling the issue of the contribution(s) of an individual researcher. In the next paper, Richard Payne and Bani Mallick provide a two- stage Metroplis-Hastings algorithm for Bayesian classification. As mentioned in the last issue, Bayesian analysis has been somewhat of a rarity in the Journal of Classification; however, along with Ligtvoet (2017), this article is the second paper in two issues that addresses Bayesian techniques, and it is a welcome addition to the panoply of methods normally covered. The goal of Payne and Mallick’s procedure is to reduce the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

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References (17)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Classification Society of North America
Subject
Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal,Image and Speech Processing; Psychometrics; Marketing
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s00357-018-9254-1
Publisher site
See Article on Publisher Site

Abstract

Journal of Classification 35:1-4 (2018) DOI: 10.1007/s00357-018-9254-1 The first paper in this issue of the Journal of Classification by Fionn Murtagh, Michael Orlov, and Boris Mirkin addresses the ever tricky issue of evaluating the quality of research produced by individuals. The approach by Murtagh and coauthors avoids many of the issues that are leveled against common metrics of productivity (like impact factor for instance, see Brumback, 2009; Brischoux and Cook, 2009; Rossner, Epps, and Hill, 2007) by taking a more holistic approach to assessing a researcher’s contributions. Personally, I believe that this is one of first efforts of what promises to be many forays into taking a broad approach at tackling the issue of the contribution(s) of an individual researcher. In the next paper, Richard Payne and Bani Mallick provide a two- stage Metroplis-Hastings algorithm for Bayesian classification. As mentioned in the last issue, Bayesian analysis has been somewhat of a rarity in the Journal of Classification; however, along with Ligtvoet (2017), this article is the second paper in two issues that addresses Bayesian techniques, and it is a welcome addition to the panoply of methods normally covered. The goal of Payne and Mallick’s procedure is to reduce the

Journal

Journal of ClassificationSpringer Journals

Published: Apr 14, 2018

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