The Annals of Statistics

Nonparametric estimation when data on derivatives are available

Peter Hall and Adonis Yatchew

Full-text: Open access

Abstract

We consider settings where data are available on a nonparametric function and various partial derivatives. Such circumstances arise in practice, for example in the joint estimation of cost and input functions in economics. We show that when derivative data are available, local averages can be replaced in certain dimensions by nonlocal averages, thus reducing the nonparametric dimension of the problem. We derive optimal rates of convergence and conditions under which dimension reduction is achieved. Kernel estimators and their properties are analyzed, although other estimators, such as local polynomial, spline and nonparametric least squares, may also be used. Simulations and an application to the estimation of electricity distribution costs are included.

Article information

Source
Ann. Statist., Volume 35, Number 1 (2007), 300-323.

Dates
First available in Project Euclid: 6 June 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1181100189

Digital Object Identifier
doi:10.1214/009053606000001127

Mathematical Reviews number (MathSciNet)
MR2332277

Zentralblatt MATH identifier
1114.62049

Subjects
Primary: 62G08: Nonparametric regression 62G20: Asymptotic properties
Secondary: 62P20: Applications to economics [See also 91Bxx] 91B38: Production theory, theory of the firm

Keywords
Dimension reduction kernel methods nonparametric regression partial derivative data rates of convergence statistical smoothing cost function estimation

Citation

Hall, Peter; Yatchew, Adonis. Nonparametric estimation when data on derivatives are available. Ann. Statist. 35 (2007), no. 1, 300--323. doi:10.1214/009053606000001127. https://projecteuclid.org/euclid.aos/1181100189


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