The Annals of Statistics

Testing for additivity in nonparametric regression

R. L. Eubank, J. D. Hart, D. G. Simpson, and L. A. Stefanski

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Abstract

Additive models are one means of assuaging the curse of dimensionality when nonparametric smoothing methods are used to estimate multivariable regression functions. It is important to have methods for testing the fit of such models, especially in high dimensions where visual assessment of fit becomes difficult. New tests of additivity are proposed in this paper that derive from Fourier series estimators with data-driven smoothing parameters. Other tests related to the classical Tukey test for additivity are also considered. While the new tests are consistent against essentially any "smooth" alternative to additivity, the Tukey-type tests are found to be inconsistent in certain situations. Asymptotic power of both varieties of tests is studied under local alternatives that tend toward additivity at a parametric rate, and small-sample power comparisons are carried out by means of a simulation study.

Article information

Source
Ann. Statist., Volume 23, Number 6 (1995), 1896-1920.

Dates
First available in Project Euclid: 15 October 2002

Permanent link to this document
https://projecteuclid.org/euclid.aos/1034713639

Digital Object Identifier
doi:10.1214/aos/1034713639

Mathematical Reviews number (MathSciNet)
MR1389857

Zentralblatt MATH identifier
0858.62036

Subjects
Primary: 62E20: Asymptotic distribution theory 62E25 62G07: Density estimation 62G10: Hypothesis testing 62G20: Asymptotic properties

Keywords
Additive models dimension reduction kernel estimators Tukey test Fourier series order-selection test local linear smoothers

Citation

Eubank, R. L.; Hart, J. D.; Simpson, D. G.; Stefanski, L. A. Testing for additivity in nonparametric regression. Ann. Statist. 23 (1995), no. 6, 1896--1920. doi:10.1214/aos/1034713639. https://projecteuclid.org/euclid.aos/1034713639


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