Minimax unbiased estimation for finite populations with bounded outcomes

TL;DR

This paper develops a minimax unbiased estimation method for finite population totals with outcomes bounded within known ranges, optimizing sampling design and estimator choice.

Contribution

It introduces a sharp lower bound on worst-case error, characterizes the minimax estimator, and proposes an optimal independent sampling strategy under constraints.

Findings

The minimax estimator is the midpoint-differenced Horvitz-Thompson estimator.

Optimal sampling probabilities are proportional to the outcome bounds.

The approach extends classical minimax results to bounded outcomes and unbiased estimators.

Abstract

We study design-unbiased estimation of the finite-population total $\sum_{i=1}^N y_i$ when each outcome satisfies known bounds $y_i\in[a_i,b_i]$. For any sampling design with inclusion probabilities $\pi_i>0$, we prove a sharp lower bound on the worst-case squared error over the rectangular parameter space. This bound is attained if and only if the unit inclusion indicators are pairwise independent, in which case the minimax estimator is the midpoint-differenced Horvitz-Thompson estimator $\sum_{i=1}^N m_i+\sum_{i\in S}(y_i-m_i)/\pi_i$, with $m_i=(a_i+b_i)/{2}$. We then solve the joint design-and-estimation problem under the constraint $\sum_i \pi_i\le n$. We find that a minimax strategy samples units independently with probabilities $\pi_i^\ast=\min(1,c (b_i-a_i))$ where $c>0$ is chosen so that $\sum_i \pi_i^\ast=n$, and uses the midpoint-differenced estimator. This extends Gabler (1990)'s linear minimax result to the full class of design-unbiased estimators. We also show that the estimator is admissible among unbiased estimators and affine equivariant.