Online Program Home
  My Program

Abstract Details

Activity Number: 252 - SPEED: Nonparametrics and Imaging
Type: Contributed
Date/Time: Monday, July 31, 2017 : 3:05 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #325347
Title: Estimation of Locally Stationary Spatial Processes with Application 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: American Community Survey ; local stationarity ; random field ; stochastic design

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.

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

Back to the full JSM 2017 program

Copyright © American Statistical Association