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December 2014 A permutational-splitting sample procedure to quantify expert opinion on clusters of chemical compounds using high-dimensional data
Elasma Milanzi, Ariel Alonso, Christophe Buyck, Geert Molenberghs, Luc Bijnens
Ann. Appl. Stat. 8(4): 2319-2335 (December 2014). DOI: 10.1214/14-AOAS772

Abstract

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. We propose a method to quantify these qualitative assessments using hierarchical models. However, with the most commonly available computing resources, the high dimensionality of the vectors of fixed effects and correlated responses renders maximum likelihood unfeasible in this scenario. We devise a reliable procedure to tackle this problem and show, using theoretical arguments and simulations, that the new methodology compares favorably with maximum likelihood, when the latter option is available. The approach was motivated by a case study, which we present and analyze.

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Elasma Milanzi. Ariel Alonso. Christophe Buyck. Geert Molenberghs. Luc Bijnens. "A permutational-splitting sample procedure to quantify expert opinion on clusters of chemical compounds using high-dimensional data." Ann. Appl. Stat. 8 (4) 2319 - 2335, December 2014. https://doi.org/10.1214/14-AOAS772

Information

Published: December 2014
First available in Project Euclid: 19 December 2014

zbMATH: 06408780
MathSciNet: MR3292499
Digital Object Identifier: 10.1214/14-AOAS772

Keywords: maximum likelihood , pseudo-likelihood , rater , split samples

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 4 • December 2014
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