Multivariate wavelet-based shape-preserving estimation for dependent observations

We introduce a new approach to shape-preserving estimation of cumulative distribution functions and probability density functions using the wavelet methodology for multivariate dependent data. Our estimators preserve shape constraints such as monotonicity, positivity and integration to one, and allow for low spatial regularity of the underlying functions. We discuss conditional quantile estimation for financial time series data as an application. Our methodology can be implemented with B-splines. We show by means of Monte Carlo simulations that it performs well in finite samples and for a datadriven choice of the resolution level.

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Document type	Article de périodique (Journal article) – Article de recherche
Publication date	2007
Language	Anglais
Journal information	"Bernoulli : a journal of mathematical statistics and probability" - Vol. 13, no. 2, p. 301-329 (2007)
Peer reviewed	yes
Publisher	Int Statistical Inst (Voorburg)
issn	1350-7265
Publication status	Publié
Affiliations	UCL - ESPO/ECON - Département des sciences économiques UCL - EUEN/STAT - Institut de statistique
Keywords	B-splines ; conditional quantile ; multivariate process ; shape preserving wavelet estimation ; time series
Links	https://doi.org/10.3150/07-BEJ5066[DOI] http://hdl.handle.net/2078.1/37497[Handle]

See also
	[boreal:91579]	Multivariate wavelet-based shape preserving estimation for dependent observations