Dimension Reduction for Multinomial Models Via a Kolmogorov-Smirnov Measure (KSM)

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dc.contributor.authorLoftus, Stephen C.en
dc.contributor.authorHouse, Leanna L.en
dc.contributor.authorHughey, Myra C.en
dc.contributor.authorWalke, Jenifer B.en
dc.contributor.authorBecker, Matthew H.en
dc.contributor.authorBelden, Lisa K.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2019-05-08T19:46:21Zen
dc.date.available2019-05-08T19:46:21Zen
dc.date.issued2015en
dc.description.abstractDue to advances in technology and data collection techniques, the number of measurements often exceeds the number of samples in ecological datasets. As such, standard models that attempt to assess the relationship between variables and a response are inapplicable and require a reduction in the number of dimensions to be estimable. Several filtering methods exist to accomplish this, including Indicator Species Analyses and Sure Information Screening, but these techniques often have questionable asymptotic properties or are not readily applicable to data with multinomial responses. As such, we propose and validate a new metric called the Kolmogorov-Smirnov Measure (KSM) to be used for filtering variables. In the paper, we develop the KSM, investigate its asymptotic properties, and compare it to group equalized Indicator Species Values through simulation studies and application to a well-known biological dataset.en
dc.format.extent19 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.sourceurlhttps://www.stat.vt.edu/content/dam/stat_vt_edu/graphics-and-pdfs/research-papers/Technical_Reports/TechReport15-1.pdfen
dc.identifier.urihttp://hdl.handle.net/10919/89423en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.ispartofseriesTechnical Report No. 15-1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleDimension Reduction for Multinomial Models Via a Kolmogorov-Smirnov Measure (KSM)en
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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