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Linear Discriminant Analysis of Multivariate Spatial–Temporal Regressions

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  • JŪRATĖ ŠALTYTĖ‐BENTH
  • KȨSTUTIS DUČINSKAS

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.

Suggested Citation

  • Jūratė Šaltytė‐Benth & Kȩstutis Dučinskas, 2005. "Linear Discriminant Analysis of Multivariate Spatial–Temporal Regressions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(2), pages 281-294, June.
    • Handle: RePEc:bla:scjsta:v:32:y:2005:i:2:p:281-294
    DOI: 10.1111/j.1467-9469.2005.00421.x
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    Cited by:

    1. Ducinskas, Ke[combining cedilla]stutis, 2009. "Approximation of the expected error rate in classification of the Gaussian random field observations," Statistics & Probability Letters, Elsevier, vol. 79(2), pages 138-144, January.

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