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2016-12-29Diskussionspapier DOI: 10.18452/18557
Multivariate Factorisable Sparse Asymmetric Least Squares Regression
dc.contributor.authorChao, Shih-Kang
dc.contributor.authorHärdle, Wolfgang Karl
dc.contributor.authorHuang, Chen
dc.date.accessioned2017-11-17T13:33:46Z
dc.date.available2017-11-17T13:33:46Z
dc.date.issued2016-12-29
dc.identifier.urihttp://edoc.hu-berlin.de/18452/19253
dc.description.abstractMore and more data are observed in form of curves. Numerous applications in finance, neuroeconomics, demographics and also weather and climate analysis make it necessary to extract common patterns and prompt joint modelling of individual curve variation. Focus of such joint variation analysis has been on fluctuations around a mean curve, a statistical task that can be solved via functional PCA. In a variety of questions concerning the above applications one is more interested in the tail asking therefore for tail event curves (TEC) studies. With increasing dimension of curves and complexity of the covariates though one faces numerical problems and has to look into sparsity related issues. Here the idea of FActorisable Sparse Tail Event Curves (FASTEC) via multivariate asymmetric least squares regression (expectile regression) in a high-dimensional framework is proposed. Expectile regression captures the tail moments globally and the smooth loss function improves the convergence rate in the iterative estimation algorithm compared with quantile regression. The necessary penalization is done via the nuclear norm. Finite sample oracle properties of the estimator associated with asymmetric squared error loss and nuclear norm regularizer are studied formally in this paper. As an empirical illustration, the FASTEC technique is applied on fMRI data to see if individual’s risk perception can be recovered by brain activities. Results show that factor loadings over different tail levels can be employed to predict individual’s risk attitudes.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjecthigh-dimensionalM-estimatoreng
dc.subjectnuclear norm regularizereng
dc.subjectfactorizationeng
dc.subjectexpectile regressioneng
dc.subjectfMRIeng
dc.subjectrisk perceptioneng
dc.subjectmultivariate functional dataeng
dc.subject.ddc330 Wirtschaft
dc.titleMultivariate Factorisable Sparse Asymmetric Least Squares Regression
dc.typeworkingPaper
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/19253-7
dc.identifier.doihttp://dx.doi.org/10.18452/18557
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages32
local.edoc.type-nameDiskussionspapier
local.edoc.institutionWirtschaftswissenschaftliche Fakultät
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2016
local.edoc.container-issue58
local.edoc.container-erstkatid2195055-6

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