Online Program Home
  My Program

Abstract Details

Activity Number: 161 - SPEED: Nonparametrics and Imaging
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #324709 View Presentation
Title: Estimation of Locally Stationary Spatial Processes with Applications to the American Community Survey
Author(s): Daniel Weinberg* and Tucker McElroy and Soumendra N Lahiri
Companies: U.S. Census Bureau and U. S. Census Bureau and North Carolina State University
Keywords: Non-Gaussian ; Kriging ; American Community Survey ; Local Stationarity

The American Community Survey (ACS) multiyear estimates provide detailed economic and demographic information at a census tract level. However, many users of the ACS require customized regional estimates, amounting to a change of support problem. The assumption of stationarity throughout the country is dubious, and so we present a formulation of local stationarity that can take into account variations of the covariance structure across census tracts. In addition, we adopt a nonparametric approach to modeling that remains agnostic about specific distributional features; for instance, we do not make a Gaussian process assumption. Covariances of larger areal units, such as counties, can be estimated from the local covariance structure of census tracts. These in turn facilitate the calculation of residuals, whereby the model adequacy can be verified. Finally, these covariance estimates can be applied via kriging formulas to generate customized estimates.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

Copyright © American Statistical Association