Resource Allocation for Statistical Estimation

TL;DR

This paper presents a framework for optimally allocating resources like sensing devices or computing power across diverse data sources to enhance statistical estimation and inference tasks.

Contribution

It introduces a general resource allocation framework tailored for heterogeneous data sources, optimizing statistical efficiency through analytical and convex optimization methods.

Findings

Optimal resource allocations can be derived in closed form or via convex optimization.

The framework improves statistical inference performance across various data collection scenarios.

Application examples include parameter estimation and hypothesis testing with heterogeneous data.

Abstract

Statistical estimation in many contemporary settings involves the acquisition, analysis, and aggregation of datasets from multiple sources, which can have significant differences in character and in value. Due to these variations, the effectiveness of employing a given resource (e.g., a sensing device or computing power) for gathering or processing data from a particular source depends on the nature of that source. As a result, the appropriate division and assignment of a collection of resources to a set of data sources can substantially impact the overall performance of an inferential strategy. In this expository article, we adopt a general view of the notion of a resource and its effect on the quality of a data source, and we describe a framework for the allocation of a given set of resources to a collection of sources in order to optimize a specified metric of statistical efficiency. We discuss several stylized examples involving inferential tasks such as parameter estimation and hypothesis testing based on heterogeneous data sources, in which optimal allocations can be computed either in closed form or via efficient numerical procedures based on convex optimization.