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dc.creatorSolís Chacón, Maikol
dc.creatorHernández Alvarado, Alberto José
dc.creatorZúñiga Rojas, Ronald Alberto
dc.date.accessioned2018-12-20T15:20:45Z
dc.date.available2018-12-20T15:20:45Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10669/76360
dc.description.abstractAbstract The course of dimensionality is a common problem in statistics and data analysis. Variable sensitivity analysis methods are a well studied and established set of tools designed to overcome these sorts of problems. However, as this work shows, these methods fail to capture relevant features and patterns hidden within the geometry of the enveloping manifold projected into a variable. We propose an index that captures, reflects and correlates the relevance of distinct variables within a model by focusing on the geometry of their projections. The analysis was made with an original R-package called TopSA, short for Topological Sensitivity Anal- ysis. The TopSA R-package is available on the site https://github.com/maikol- solis/TopSA.es_ES
dc.language.isoeses_ES
dc.subjectHomologyes_ES
dc.subjectTopological manifoldses_ES
dc.subjectSensitivity Analysises_ES
dc.subjectSimplexeses_ES
dc.subject510 Matemáticases_ES
dc.titleGeometrical correlation indices using homological constructions on manifoldses_ES
dc.typeinfo:eu-repo/semantics/preprintes_ES
dc.description.procedenceUCR::Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones en Matemáticas Puras y Aplicadas (CIMPA)es_ES
dc.description.procedenceUCR::Investigación::Unidades de Investigación::Ciencias Básicas::Centro de investigaciones Matemáticas y Metamatemáticas (CIMM)es_ES


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