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Published January 2016 | public
Journal Article

Resource Allocation for Statistical Estimation


Statistical estimation in many contemporary settings involves the acquisition, analysis, and aggregation of data sets 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. This work is an inferential analog of the literature in information theory on allocating power across communications channels of variable quality in order to optimize for total throughput.

Additional Information

© 2015 IEEE. Manuscript received December 17, 2014; revised April 21, 2015; accepted July 2, 2015. Date of publication December 10, 2015; date of current version December 18, 2015.

Additional details

August 20, 2023
October 17, 2023