Maximum-likelihood estimation of the random-clumped multinomial model as a prototype problem for large-scale statistical computing

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dc.contributor.authorRaim, Andrew M.
dc.contributor.authorGobbert, Matthias K.
dc.contributor.authorNeerchal, Nagaraj K.
dc.contributor.authorMorel, Jorge G.
dc.date.accessioned2018-10-24T18:21:09Z
dc.date.available2018-10-24T18:21:09Z
dc.date.issued2012-05-08
dc.description.abstractNumerical methods are needed to obtain maximum-likelihood estimates (MLEs) in many problems. Computation time can be an issue for some likelihoods even with modern computing power. We consider one such problem where the assumed model is a random-clumped multinomial distribution. We compute MLEs for this model in parallel using the Toolkit for Advanced Optimization software library. The computations are performed on a distributed-memory cluster with low latency interconnect. We demonstrate that for larger problems, scaling the number of processes improves wall clock time significantly. An illustrative example shows how parallel MLE computation can be useful in a large data analysis. Our experience with a direct numerical approach indicates that more substantial gains may be obtained by making use of the specific structure of the random-clumped modelen_US
dc.description.sponsorshipThe facility is supported by the U.S. National Science Foundation through the MRI program (grant no. CNS–0821258) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See http://www.umbc.edu/hpcf for more information on HPCF and the projects using its resources. The first author additionally acknowledges financial support as HPCF RA.en_US
dc.description.urihttps://www.tandfonline.com/doi/abs/10.1080/00949655.2012.684095en_US
dc.format.extent20 pagesen_US
dc.genrejournal article pre-printen_US
dc.identifierdoi:10.13016/M2707WS4G
dc.identifier.citationAndrew M. Raim, Matthias K. Gobbert, Nagaraj K. Neerchal & Jorge G. Morel (2013) Maximum-likelihood estimation of the random-clumped multinomial model as a prototype problem for large-scale statistical computing, Journal of Statistical Computation and Simulation, 83:12, 2178-2194, DOI: 10.1080/00949655.2012.684095en_US
dc.identifier.urihttps://doi.org/10.1080/00949655.2012.684095
dc.identifier.urihttp://hdl.handle.net/11603/11671
dc.language.isoen_USen_US
dc.publisherTaylor and Francis Onlineen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsThis is the submitted Manuscript of an article published by Taylor & Francis in Journal of Statistical Computation and Simulation on 2012-05-08, available online: http://www.tandfonline.com/10.1080/00949655.2012.684095.
dc.subjectparallel computingen_US
dc.subjectmaximum likelihood estimationen_US
dc.subjectmixture distributionen_US
dc.subjectmultinomialen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)
dc.titleMaximum-likelihood estimation of the random-clumped multinomial model as a prototype problem for large-scale statistical computingen_US
dc.typeTexten_US

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