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

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  • Dučinskas, Kęstutis
  • Dreižienė, Lina
  • Zikarienė, Eglė

Abstract

Given training sample, the problem of classifying the scalar Gaussian random field observation into one of several classes specified by different regression mean models and common parametric covariance function is considered. The classifier based on the plug-in Bayes classification rule formed by replacing unknown parameters in Bayes classification rule with their ML estimators is investigated. This is the extension of the previous one from the two-class case to the multiclass case. The novel close form expressions for the actual error rate and approximation of the expected error rate incurred by proposed classifier are derived. These error rates are suggested as performance measures for the proposed classifier.

Suggested Citation

  • Dučinskas, Kęstutis & Dreižienė, Lina & Zikarienė, Eglė, 2015. "Multiclass classification of the scalar Gaussian random field observation with known spatial correlation function," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 107-114.
    • Handle: RePEc:eee:stapro:v:98:y:2015:i:c:p:107-114
    DOI: 10.1016/j.spl.2014.12.008
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    References listed on IDEAS

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    1. Markus Abt, 1999. "Estimating the Prediction Mean Squared Error in Gaussian Stochastic Processes with Exponential Correlation Structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(4), pages 563-578, December.
    2. A. Batsidis & K. Zografos, 2011. "Errors of misclassification in discrimination of dimensional coherent elliptic random field observations," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 65(4), pages 446-461, November.
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