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Resource Allocation for Statistical Estimation

Berthet, Quentin and Chandrasekaran, Venkat (2014) Resource Allocation for Statistical Estimation. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190702-091807009

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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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1412.6613arXivDiscussion Paper
Subject Keywords:Heterogeneous data sources, assignment problems, convex programming, resource tradeoffs in statistical estimation
Record Number:CaltechAUTHORS:20190702-091807009
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190702-091807009
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96876
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Jul 2019 17:51
Last Modified:08 Jul 2019 17:51

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