Linear Discriminant Analysis of Multivariate Spatial-Temporal Regressions

Abstract

We consider classification of the realization of a multivariate spatial-temporal Gaussian random field into one of two populations with different regression mean models and factorized covariance matrices. Unknown means and common feature vector covariance matrix are estimated from training samples with observations correlated in space and time, assuming spatial-temporal correlations to be known. We present the first-order asymptotic expansion of the expected error rate associated with a linear plug-in discriminant function. Our results are applied to ecological data collected from the Lithuanian Economic Zone in the Baltic Sea. Copyright 2005 Board of the Foundation of the Scandinavian Journal of Statistics..

A simple approach for statistical modelling of ice phenomena in the Curonian Lagoon, the south-eastern Baltic Sea

“…Other validation indices may provide additional information about the accuracy of the used models. (Dučinskas & Šaltytė-Benth 2005). The predictions carried out in the stations with different distances underline the importance of spatial information in such projections.…”

Error Rates in Multi-category Classification of the Spatial Multivariate Gaussian Data

“…Šaltyt -Benth and Du inskas [1], Du inskas [2], Batsidis and Zografos [3]). Multi-category linear discriminant analysis of spatial data generated by univariate Gaussian random field (GRF) is considered in Du inskas et al [4].…”

Multiclass classification of the scalar Gaussian random field observation with known spatial correlation function

“…Šaltytė and Dučinskas (2002) derived the asymptotic approximation of the ER when classifying the observation of a univariate Gaussian random field into one of two classes with different regression mean models and common variance. This result was generalized to multivariate spatial-temporal regression model in Šaltytė-Benth and Dučinskas (2005). However, the observations to be classified are assumed to be independent from training samples in all publications listed above.…”