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Daniel Weinberg

U.S. Census Bureau



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Tucker McElroy

U.S. Census Bureau



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Soumendra Lahiri

North Carolina State University



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252 – SPEED: Nonparametrics and Imaging

Estimation of Locally Stationary Spatial Processes with Application to the American Community Survey

Sponsor: Section on Nonparametric Statistics
Keywords: American Community Survey, local stationarity, random field, stochastic design

Daniel Weinberg

U.S. Census Bureau

Tucker McElroy

U.S. Census Bureau

Soumendra Lahiri

North Carolina State University

The American Community Survey (ACS) multiyear estimates provide detailed economic and demographic information at a census tract level. The assumption of spatial stationarity for many variables is dubious, which motivates our formulation of local stationarity that can take into account changes in the covariance structure across census tracts. In addition, we adopt a nonparametric modeling approach that remains agnostic about specific distributional features. We present fairly general constructions in both the frequency and spatial domains, deriving an estimator for the local covariance. The properties of the local covariance estimator are explored through simulation. For our application, we utilize our estimator on the ACS data of median household income in the state of Iowa.

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