The Annals of Statistics

Minimax Bayes Estimation in Nonparametric Regression

Nancy E. Heckman and Michael Woodroofe

Full-text: Open access

Abstract

One observes $n$ data points, $(\mathbf{t}_i, Y_i),$ with the mean of $Y_i$, conditional on the regression function $f,$ equal to $f(\mathbf{t}_i).$ The prior distribution of the vector $\mathbf{f} = (f(\mathbf{t}_1), \ldots, f(\mathbf{t}_n))^t$ is unknown, but lies in a known class $\Omega.$ An estimator, $\hat{\mathbf{f}},$ of $\mathbf{f}$ is found which minimizes the maximum $E\|\hat{\mathbf{f}} - \mathbf{f}\|^2.$ The maximum is taken over all priors in $\Omega$ and the minimum is taken over linear estimators of $\mathbf{f}.$ Asymptotic properties of the estimator are studied in the case that $\mathbf{t}_i$ is one-dimensional and $\Omega$ is the set of priors for which $f$ is smooth.

Article information

Source
Ann. Statist., Volume 19, Number 4 (1991), 2003-2014.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176348383

Digital Object Identifier
doi:10.1214/aos/1176348383

Mathematical Reviews number (MathSciNet)
MR1135161

Zentralblatt MATH identifier
0747.62014

JSTOR
links.jstor.org

Subjects
Primary: 65D10: Smoothing, curve fitting

Keywords
Minimax estimates Bayes estimates nonparametric regression smoothing

Citation

Heckman, Nancy E.; Woodroofe, Michael. Minimax Bayes Estimation in Nonparametric Regression. Ann. Statist. 19 (1991), no. 4, 2003--2014. doi:10.1214/aos/1176348383. https://projecteuclid.org/euclid.aos/1176348383


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