Modeling and exploring multivariate spatial variation: A test procedure for isotropy of multivariate spatial data

DOI10.1006/jmva.2000.1938zbMath0997.62043OpenAlexW2009387431WikidataQ57014456 ScholiaQ57014456MaRDI QIDQ5943757

Giovanna Jona Lasinio

Publication date: 17 September 2001

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.2000.1938

zbMATH Keywords

principal component analysisisotropycross-variogrammultivariate spatial random field

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Random fields; image analysis (62M40) Factor analysis and principal components; correspondence analysis (62H25) Hypothesis testing in multivariate analysis (62H15)

Related Items (3)

Selection of the neighborhood structure for space-time Markov random field models ⋮ A review of nonparametric hypothesis tests of isotropy properties in spatial data ⋮ Assessing Isotropy for Spatial Point Processes

Cites Work

• The intrinsic random functions and their applications
• Fitting matrix-valued variogram models by simultaneous diagonalization. I: Theory
• Fitting matrix-valued variogram models by simultaneous diagonalization. II: Application
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