Ridge regression and asymptotic minimax estimation over spheres of growing dimension

Lee H. Dicker

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We study asymptotic minimax problems for estimating a $d$-dimensional regression parameter over spheres of growing dimension ($d\to\infty$). Assuming that the data follows a linear model with Gaussian predictors and errors, we show that ridge regression is asymptotically minimax and derive new closed form expressions for its asymptotic risk under squared-error loss. The asymptotic risk of ridge regression is closely related to the Stieltjes transform of the Marčenko–Pastur distribution and the spectral distribution of the predictors from the linear model. Adaptive ridge estimators are also proposed (which adapt to the unknown radius of the sphere) and connections with equivariant estimation are highlighted. Our results are mostly relevant for asymptotic settings where the number of observations, $n$, is proportional to the number of predictors, that is, $d/n\to\rho\in(0,\infty)$.

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Bernoulli, Volume 22, Number 1 (2016), 1-37.

Received: January 2013
Revised: September 2013
First available in Project Euclid: 30 September 2015

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adaptive estimation equivariance Marčenko–Pastur distribution random matrix theory


Dicker, Lee H. Ridge regression and asymptotic minimax estimation over spheres of growing dimension. Bernoulli 22 (2016), no. 1, 1--37. doi:10.3150/14-BEJ609.

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