Jun Zhu
University of Wisconsin, Madison

Identification of clusters in space based on lattice data

For the purpose of grouping spatial units on a lattice with similar characteristics within a group but with distinction among groups, we consider spatial cluster detection and change-set analysis. While the existing methods for spatial cluster detection are largely based on hypothesis testing or Bayesian models, we consider an alternative frequentist approach using regularization. In addition, we develop a change-set method for two-dimensional spatial data that permit more abrupt changes in space and irregular change sets. A quasi-likelihood approach is taken for statistical inference that accounts for covariates and spatial correlation. Finite-sample properties are investigated in a simulation study and the methods are applied to analyze county-based poverty rates in the Upper Midwest.

Refreshments at 3:45pm in Snedecor 2101.