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Social and Information Sciences Laboratory (SISL) Seminar

Friday, June 7, 2019
12:00pm to 1:00pm
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Baxter 127
Optimal Data Acquisition for Statistical Estimation
Juba Ziani, Graduate Student, Department of Computing and Mathematical Sciences, Caltech,

Abstract: We consider a data analyst's problem of purchasing data from strategic agents to compute an unbiased estimate of a statistic of interest. Agents incur private costs to reveal their data and the costs can be arbitrarily correlated with their data. Once revealed, data are verifiable. This paper focuses on linear unbiased estimators. We design an individually rational and incentive compatible mechanism that optimizes the worst-case mean-squared error of the estimation, where the worst-case is over the unknown correlation between costs and data, subject to a budget constraint in expectation. We characterize the form of the optimal mechanism in closed-form. We further extend our results to acquiring data for estimating a parameter in regression analysis, where private costs can correlate with the values of the dependent variable but not with the values of the independent variables.

For more information, please contact Mary Martin by phone at 626-395-4571 or by email at [email protected].