## Abstract

When estimating the coefficients in a linear regression it is usually assumed that the covariances of the observations on the dependent variable are known up to multiplication by some common positive number, say $c$, which is unknown. If this number $c$ is known to be less than some number $k$, and if the set of possible distributions of the dependent variable includes "enough" normal distributions (in a sense to be specified later) then the minimum variance linear unbiased estimators of the regression coefficients (see [1]) are minimax among the set of all estimators; furthermore these minimax estimators are independent of the value of $k$. (The risk for any estimator is here taken to be the expected square of the error.) This fact is closely related to a theorem of Hodges and Lehmann ([3], Theorem 6.5), stating that if the observations on the dependent variable are assumed to be independent, with variances not greater than $k$, then the minimum variance linear estimators corresponding to the assumption of equal variances are minimax. For example, if a number of observations are assumed to be independent, with common (unknown) mean, and common (unknown) variance that is less than $k$; and if, for every possible value of the mean, the set of possible distributions of the observations includes the normal distribution with that mean and with variance equal to $k$; then the sample mean is the minimax estimator of the mean of the distribution. The assumption of independence with common unknown variance is, of course, essentially no less general than the assumption that the covariances are known up to multiplication by some common positive number, since the latter situation can be reduced to the former by a suitable rotation of the coordinate axes (provided that the original matrix of covariances is non-singular). This note considers the problem of minimax estimation, in the general "linear regression" framework, when less is known about the covariances of the observations on the "dependent variable" than in the traditional situation just described. For example, one might not be sure that these observations are independent, nor feel justified in assuming any other specific covariance structure. It is immediately clear that, from a minimax point of view, one cannot get along without any prior information at all about the covariances, for in that case the risk of every estimator is unbounded. In practice, however, one is typically willing to grant that the covariances are bounded somehow, but one may not have a very precise idea of the nature of the bound. One is therefore led to look for different ways of bounding the covariances, in the hope that the minimax estimators are not too sensitive to the bound. Unfortunately, in the directions explored here, the minimax estimator is sensitive to the "form" of the bound, although once the form has been chosen the minimax estimator does not depend on the "magnitude" of the bound. This result thus provides an instance in which the minimax principle is not too effective against the difficulties due to vagueness of the statistical assumptions of a problem, although this is a type of situation in which it has often been successful (see Savage in [4], pp. 168-9). In this note, two ways of bounding the covariances are considered. The first is equivalent to choosing a coordinate system for the "dependent variables," and placing a bound on the characteristic roots of the matrix of covariances of the coordinates, in terms of one of a certain class of metrics (e.g., placing a bound on the trace on the covariance matrix, or on its largest characteristic root). The second way consists of choosing a coordinate system, and then placing a bound on the variance of each coordinate. In the first situation, the minimum variance linear unbiased estimator corresponding to the case of uncorrelated coordinates, with equal variances, turns out to be minimax; this minimax estimator is, in general, different for different choices of coordinate system, but does not depend on the "magnitude" of the bound. Also, the minimax loss typically decreases at the rate of the reciprocal of the sample size. In the second situation, the minimax procedures derived here involve ignoring most of the observations, and applying a linear unbiased estimator to the rest. Again, the minimax procedure depends upon the choice of coordinate system; furthermore, in this case the minimax loss typically either does not approach zero with increasing sample size, or does so much more slowly than the reciprocal of the sample size. Thus the minimax estimator appears to be less unsatisfactory in the first situation than in the second, but in both cases it depends upon the choice of coordinate system, which is a disadvantage if there is no "natural" coordinate system intrinsic to the regression problem being considered. Section 2 below presents the formulation of the problem, and a basic lemma. Sections 3 and 4 explore the two ways of bounding the covariances just mentioned. Some examples are given in Section 5. I am indebted to R. R. Bahadur, L. J. Savage, and G. Debreu for their helpful comments.

## Citation

R. Radner. "Minimax Estimation for Linear Regressions." Ann. Math. Statist. 29 (4) 1244 - 1250, December, 1958. https://doi.org/10.1214/aoms/1177706455

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