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http://hdl.handle.net/1942/18648
Title: | A permutational-splitting sample procedure to quantify expert opinion on clusters of chemical compounds using high-dimensional data | Authors: | MILANZI, Elasma ALONSO ABAD, Ariel Buyck, Christophe MOLENBERGHS, Geert BIJNENS, Luc |
Issue Date: | 2014 | Source: | Annals of Applied Statistics, 8 (4), p. 2319-2335 | 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. | Notes: | E-mail Addresses:elasma.milanzi@uhasselt.be; ariel.alonso@maastrichtuniversity.nl; cbuyck@its.jnj.com; geert.molenberghs@uhasselt.be; lbijnens@its.jnj.com | Keywords: | maximum likelihood; pseudo-likelihood; rater; split samples | Document URI: | http://hdl.handle.net/1942/18648 | Link to publication/dataset: | https://arxiv.org/pdf/1502.00754.pdf | ISSN: | 1932-6157 | e-ISSN: | 1941-7330 | DOI: | 10.1214/14-AOAS772 | ISI #: | 000347530200020 | Rights: | © Institute of Mathematical Statistics, 2014. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2016 |
Appears in Collections: | Research publications |
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AOAS772_Final.pdf Restricted Access | Published version | 190.18 kB | Adobe PDF | View/Open Request a copy |
1502.00754.pdf | Peer-reviewed author version | 278.29 kB | Adobe PDF | View/Open |
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