Spatial Blind Source Separation

Abstract

Recently a blind source separation model was suggested for spatial data together with an estimator based on the simultaneous diagonalisation of two scatter matrices. The asymptotic properties of this estimator are derived here and a new estimator, based on the joint diagonalisation of more than two scatter matrices, is proposed. The asymptotic properties and merits of the novel estimator are verified in simulation studies. A real data example illustrates the method.

References in corpus (4)

• Cross-Covariance Functions for Multivariate Geostatistics
• Separation of uncorrelated stationary time series using autocovariance matrices
• Determining the signal dimension in second order source separation
• Fast tensorial JADE

Cited by in corpus (10)

• Graph Signal Processing Meets Blind Source Separation
• Blind source separation for non-stationary random fields
• Modelling multivariate spatio-temporal data with identifiable variational autoencoders
• Test of the Latent Dimension of a Spatial Blind Source Separation Model
• Nonlinear blind source separation exploiting spatial nonstationarity
• Spatial Blind Source Separation in the Presence of a Drift
• Sliced Inverse Regression for Spatial Data
• Data Type Agnostic Visual Sensitivity Analysis
• On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction
• Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals