Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22555
Authors: 
Dette, Holger
Pilz, Kay F.
Year of Publication: 
2004
Series/Report no.: 
Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2004,42
Abstract: 
A monotone estimate of the conditional variance function in a heteroscedastic, nonpara- metric regression model is proposed. The method is based on the application of a kernel density estimate to an unconstrained estimate of the variance function and yields an esti- mate of the inverse variance function. The final monotone estimate of the variance function is obtained by an inversion of this function. The method is applicable to a broad class of nonparametric estimates of the conditional variance and particularly attractive to users of conventional kernel methods, because it does not require constrained optimization techniques. The approach is also illustrated by means of a simulation study
Subjects: 
nonparametric regression
heteroscedasticity
variance function
monotonicity
order restricted inference
Document Type: 
Working Paper

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