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Sliced Inverse Regression for Spatial Data

In: Festschrift in Honor of R. Dennis Cook

Author

Listed:
  • Christoph Muehlmann

    (Vienna University of Technology, Institute of Statistics & Mathematical Methods in Economics)

  • Hannu Oja

    (University of Turku, Department of Mathematics and Statistics)

  • Klaus Nordhausen

    (Vienna University of Technology, Institute of Statistics & Mathematical Methods in Economics)

Abstract

Sliced inverse regression is one of the most popular sufficient dimension reduction methods. Originally, it was designed for independent and identically distributed data and recently extend to the case of serially and spatially dependent data. In this work we extend it to the case of spatially dependent data where the response might depend also on neighbouring covariates when the observations are taken on a grid-like structure as it is often the case in econometric spatial regression applications. We suggest guidelines on how to decide upon the dimension of the subspace of interest and also which spatial lag might be of interest when modeling the response. These guidelines are supported by a conducted simulation study.

Suggested Citation

  • Christoph Muehlmann & Hannu Oja & Klaus Nordhausen, 2021. "Sliced Inverse Regression for Spatial Data," Springer Books, in: Efstathia Bura & Bing Li (ed.), Festschrift in Honor of R. Dennis Cook, pages 87-107, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-69009-0_5
    DOI: 10.1007/978-3-030-69009-0_5
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