The Annals of Statistics

Rodeo: Sparse, greedy nonparametric regression

John Lafferty and Larry Wasserman

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We present a greedy method for simultaneously performing local bandwidth selection and variable selection in nonparametric regression. The method starts with a local linear estimator with large bandwidths, and incrementally decreases the bandwidth of variables for which the gradient of the estimator with respect to bandwidth is large. The method—called rodeo (regularization of derivative expectation operator)—conducts a sequence of hypothesis tests to threshold derivatives, and is easy to implement. Under certain assumptions on the regression function and sampling density, it is shown that the rodeo applied to local linear smoothing avoids the curse of dimensionality, achieving near optimal minimax rates of convergence in the number of relevant variables, as if these variables were isolated in advance.

Article information

Ann. Statist. Volume 36, Number 1 (2008), 28-63.

First available: 1 February 2008

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G08: Nonparametric regression
Secondary: 62G20: Asymptotic properties

Nonparametric regression sparsity local linear smoothing bandwidth estimation variable selection minimax rates of convergence


Lafferty, John; Wasserman, Larry. Rodeo: Sparse, greedy nonparametric regression. The Annals of Statistics 36 (2008), no. 1, 28--63. doi:10.1214/009053607000000811.

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