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Statistical Modeling of Trivariate Static Systems: Isotonic Models

1,*,† and 2,†
1
Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche (UnivPM), 60131 Ancona, Italy
2
School of Information and Automation Engineering, Università Politecnica delle Marche (UnivPM), 60131 Ancona, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 27 November 2018 / Revised: 9 January 2019 / Accepted: 17 January 2019 / Published: 21 January 2019

Abstract

This paper presents an improved version of a statistical trivariate modeling algorithm introduced in a short Letter by the first author. This paper recalls the fundamental concepts behind the proposed algorithm, evidences its criticalities and illustrates a number of improvements which lead to a functioning modeling algorithm. The present paper also illustrates the features of the improved statistical modeling algorithm through a comprehensive set of numerical experiments performed on four synthetic and five natural datasets. The obtained results confirm that the proposed algorithm is able to model the considered synthetic and the natural datasets faithfully. View Full-Text
Keywords: data modeling; statistical data modeling; trivariate static systems; isotonic modeling data modeling; statistical data modeling; trivariate static systems; isotonic modeling
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Fiori, S.; Vitali, A. Statistical Modeling of Trivariate Static Systems: Isotonic Models. Data 2019, 4, 17.

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