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dc.contributor.authorPacella, Massimo
dc.date.accessioned2018-12-12T11:47:02Z
dc.date.available2018-12-12T11:47:02Z
dc.date.issued2018-05-29
dc.identifier.urihttp://hdl.handle.net/10556/3016
dc.identifier.urihttp://dx.doi.org/10.14273/unisa-1306
dc.description2016 - 2017it_IT
dc.description.abstractHigh dimensional data analysis has become a popular research topic in the recent years, due to the emergence of various new applications in several fields of sciences underscoring the need for analysing massive data sets. One of the main challenge in analysing high dimensional data regards the interpretability of estimated models as well as the computational efficiency of procedures adopted. Such a purpose can be achieved through the identification of relevant variables that really affect the phenomenon of interest, so that effective models can be subsequently constructed and applied to solve practical problems. The first two chapters of the thesis are devoted in studying high dimensional statistics for variable selection. We firstly introduce a short but exhaustive review on the main developed techniques for the general problem of variable selection using nonparametric statistics. Lastly in chapter 3 we will present our proposal regarding a feature screening approach for non additive models developed by using of conditional information in the estimation procedure... [edited by Author]it_IT
dc.language.isoenit_IT
dc.publisherUniversita degli studi di Salernoit_IT
dc.subjectHigh-dimensional datait_IT
dc.subjectVariable selectionit_IT
dc.subjectSpatio-temporal modelsit_IT
dc.titleHigh-dimensional statistics for complex datait_IT
dc.typeDoctoral Thesisit_IT
dc.subject.miurSECS-S/01 STATISTICAit_IT
dc.contributor.coordinatoreDestefanis, Sergio Pietroit_IT
dc.description.cicloXXX cicloit_IT
dc.contributor.tutorGiordano, Francescoit_IT
dc.identifier.DipartimentoScienze Economiche e Statisticheit_IT
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