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http://hdl.handle.net/1942/18648
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DC Field | Value | Language |
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dc.contributor.author | MILANZI, Elasma | - |
dc.contributor.author | ALONSO ABAD, Ariel | - |
dc.contributor.author | Buyck, Christophe | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.contributor.author | BIJNENS, Luc | - |
dc.date.accessioned | 2015-04-10T07:40:22Z | - |
dc.date.available | 2015-04-10T07:40:22Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Annals of Applied Statistics, 8 (4), p. 2319-2335 | - |
dc.identifier.issn | 1932-6157 | - |
dc.identifier.uri | http://hdl.handle.net/1942/18648 | - |
dc.description.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. | - |
dc.description.sponsorship | The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government – department EWI | - |
dc.language.iso | en | - |
dc.rights | © Institute of Mathematical Statistics, 2014. | - |
dc.subject.other | maximum likelihood; pseudo-likelihood; rater; split samples | - |
dc.title | A permutational-splitting sample procedure to quantify expert opinion on clusters of chemical compounds using high-dimensional data | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 2335 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 2319 | - |
dc.identifier.volume | 8 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | E-mail Addresses:elasma.milanzi@uhasselt.be; ariel.alonso@maastrichtuniversity.nl; cbuyck@its.jnj.com; geert.molenberghs@uhasselt.be; lbijnens@its.jnj.com | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.type.programme | VSC | - |
dc.identifier.doi | 10.1214/14-AOAS772 | - |
dc.identifier.isi | 000347530200020 | - |
dc.identifier.url | https://arxiv.org/pdf/1502.00754.pdf | - |
item.contributor | MILANZI, Elasma | - |
item.contributor | ALONSO ABAD, Ariel | - |
item.contributor | Buyck, Christophe | - |
item.contributor | MOLENBERGHS, Geert | - |
item.contributor | BIJNENS, Luc | - |
item.validation | ecoom 2016 | - |
item.fullcitation | MILANZI, Elasma; ALONSO ABAD, Ariel; Buyck, Christophe; MOLENBERGHS, Geert & BIJNENS, Luc (2014) A permutational-splitting sample procedure to quantify expert opinion on clusters of chemical compounds using high-dimensional data. In: Annals of Applied Statistics, 8 (4), p. 2319-2335. | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 1932-6157 | - |
crisitem.journal.eissn | 1941-7330 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
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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