http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Variance function estimation in regression
Journal of the Royal Statistical Society
We consider estimation of a variance function g in regression problems. Such estimation requires simultaneous estimation of the mean function f. We obtain sharp results on the extent to which the smoothness of f influences best rates of convergence for estimating g. For example, in nonparametric regression with two derivatives on g, "classical" rates of convergence are possible if and only if the unknown f satisfies a Lipschitz condition of order 1/3 or more. If a parametric model is known for g, then g may be estimated n^1/2-consistently if and only if f is Lipschitz of order 1/2 or more. Optimal rates of convergence are attained by kernel estimators.
The Cushing/Texas A&M Pubs. copy is missing leaf 14.
Offprint: Journal of the Royal Statistical Society. Series B. Volume 51, pages 3-14.
Includes bibliographical references (leaf 15).
Technical report (Texas A & M University. Department of Statistics) ; no. 4