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AStA Advances in Statistical Analysis

, Volume 102, Issue 4, pp 537–563 | Cite as

A modified generalized lasso algorithm to detect local spatial clusters for count data

  • Hosik Choi
  • Eunjung Song
  • Seung-sik Hwang
  • Woojoo Lee
Original Paper
  • 202 Downloads

Abstract

Detecting local spatial clusters for count data is an important task in spatial epidemiology. Two broad approaches—moving window and disease mapping methods—have been suggested in some of the literature to find clusters. However, the existing methods employ somewhat arbitrarily chosen tuning parameters, and the local clustering results are sensitive to the choices. In this paper, we propose a penalized likelihood method to overcome the limitations of existing local spatial clustering approaches for count data. We start with a Poisson regression model to accommodate any type of covariates, and formulate the clustering problem as a penalized likelihood estimation problem to find change points of intercepts in two-dimensional space. The cost of developing a new algorithm is minimized by modifying an existing least absolute shrinkage and selection operator algorithm. The computational details on the modifications are shown, and the proposed method is illustrated with Seoul tuberculosis data.

Keywords

Spatial clustering Penalized likelihood Generalized LASSO Poisson regression 

Notes

Acknowledgements

This research was supported by INHA UNIVERSITY Research Grant.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Hosik Choi
    • 1
  • Eunjung Song
    • 2
  • Seung-sik Hwang
    • 3
  • Woojoo Lee
    • 2
  1. 1.Department of Applied StatisticsKyonggi UniversitySuwonKorea
  2. 2.Department of StatisticsInha UniversityIncheonKorea
  3. 3.Department of Public Health ScienceGraduate School of Public Health, Seoul National UniversitySeoulRepublic of Korea

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