NDLI: On generalized elliptical quantiles in the nonlinear quantile regression setup

Content Provider	Springer Nature Link
Author	Hlubinka, Daniel Šiman, Miroslav
Copyright Year	2014
Abstract	Inspired by nonlinear quantile regression, the article introduces, investigates, discusses, and illustrates a new concept of generalized elliptical location quantiles. They may require less stringent moment assumptions, be less sensitive to outliers, be less rigid, employ more a priori information regarding the location of the distribution, and have higher potential for various regression generalizations than their common elliptical predecessor defined in the convex optimization framework by means of standard linear quantile regression. Furthermore, they still include an equivalent of their predecessor as a special case and inherit most of its favorable features such as the probability interpretation, natural equivariance properties, and good behavior for elliptical and symmetric distributions, which is demonstrated both by theoretical results and data examples with convincing graphical output. On the other hand, the new elliptical quantiles need not always be uniquely defined and they require somewhat different approach to their analysis and computation due to their intrinsically non-convex formulation.
Starting Page	249
Ending Page	264
Page Count	16
File Format	PDF
ISSN	11330686
Journal	Test
Volume Number	24
Issue Number	2
e-ISSN	18638260
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-10-18
Publisher Institution	Spanish Society of Statistics and Operations Research
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multivariate quantile Elliptical quantile Quantile regression Multivariate statistical inference Portfolio optimization Characterization and structure theory Tolerance and confidence regions Statistics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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