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September 2013 Sliced inverse regression and independence in random marked sets with covariates
Ondřej} Šedivý, Jakub Stanek, Blažena Kratochvílová, Viktor Beneš
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Adv. in Appl. Probab. 45(3): 626-644 (September 2013). DOI: 10.1239/aap/1377868532

Abstract

Dimension reduction of multivariate data was developed by Y. Guan for point processes with Gaussian random fields as covariates. The generalization to fibre and surface processes is straightforward. In inverse regression methods, we suggest slicing based on geometrical marks. An investigation of the properties of this method is presented in simulation studies of random marked sets. In a refined model for dimension reduction, the second-order central subspace is analyzed in detail. A real data pattern is tested for independence of a covariate.

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Ondřej} Šedivý. Jakub Stanek. Blažena Kratochvílová. Viktor Beneš. "Sliced inverse regression and independence in random marked sets with covariates." Adv. in Appl. Probab. 45 (3) 626 - 644, September 2013. https://doi.org/10.1239/aap/1377868532

Information

Published: September 2013
First available in Project Euclid: 30 August 2013

zbMATH: 1292.60015
MathSciNet: MR3102465
Digital Object Identifier: 10.1239/aap/1377868532

Subjects:
Primary: 60D05
Secondary: 62M30

Rights: Copyright © 2013 Applied Probability Trust

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Vol.45 • No. 3 • September 2013
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