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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..

Relying on the results of multivariate analysis of the re-analysis case studies from the BaltCoast project, specific features of integrated coastal management (ICM) approaches in Estonia, Latvia, Lithuania, and the Kaliningrad Oblast of the Russian Federation are highlighted in this paper. Eleven Eastern Baltic ICM case studies have been re-analyzed in-depth, which was the main focus of the present paper, covering a wide range of coastal landscapes, themes, policy issues, and ICM approaches. Five principal components explaining 84.86% of the total variance of ICM factor scores have been elicited by calculating rotation sums of squared loadings: (1) Stakeholder Involvement; (2) Research Base; (3) Planning Consistency; (4) Policy Environment, and (5) Development of Plans/Strategies. A truly dedicated involvement of a wide range of stakeholders throughout all process stages showed to be critical for further successful advance of ICM processes and principles based on the systems approach in the Baltic States and the Russian Federation.

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