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Soil texture prediction via reduced K-means Principal Component Multinomial Regression

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  • Lucadamo, Antonio
  • Amenta, Pietro
  • Leone, Natalia

Abstract

Texture is one of the most important physical property of the soils for its influence on other fundamental properties. It is defined according to particle size distribution, that can be accurately measured in laboratory. However, these measurements are costly and very time consuming, therefore valid alternatives are necessary. In last years some statistical techniques have been used to predict textural classification using values of reflectance spectrometry as explicative variables. The estimation of the model parameters can be not too accurate, affecting prediction when there is multicollinearity among predictors. Another issue can be the great number of explicative variables usually necessary to explain the response. In order to improve the accuracy of the prediction in classification problems under multicollinearity and to reduce the dimension of the problem with continuous covariates, in this paper we introduce a new technique, based on classification and dimension reduction methods. We show how the new proposal can improve the accuracy of prediction, considering a problem concerning the textural classification of soils of Campania region.

Suggested Citation

  • Lucadamo, Antonio & Amenta, Pietro & Leone, Natalia, 2021. "Soil texture prediction via reduced K-means Principal Component Multinomial Regression," Socio-Economic Planning Sciences, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:soceps:v:75:y:2021:i:c:s0038012119304793
    DOI: 10.1016/j.seps.2020.100871
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    References listed on IDEAS

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    1. Roberto Rocci & Stefano Gattone & Maurizio Vichi, 2011. "A New Dimension Reduction Method: Factor Discriminant K-means," Journal of Classification, Springer;The Classification Society, vol. 28(2), pages 210-226, July.
    2. Vichi, Maurizio & Kiers, Henk A. L., 2001. "Factorial k-means analysis for two-way data," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 49-64, July.
    3. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
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