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.
Highlights
The availability of large datasets in the scientific literature calls for novel and efficient algorithms to build models that are potentially able to explain the complex non-linear relationships between attributes [1]
Illustrations come, for example, from materials science [16], where the elasticity of a polypropilene composite reinforced with natural fibers is modeled in terms of the geometric characteristics, namely length and diameter of the embedded fibers, as well as from bioenergy analysis [17], where the smoke emission of a compression ignition engine fed with biomass-derived fuel is modeled in terms of injection timing and biomass blend ratio
Further illustrations are found in [18], where the effects of pharmacological interventions that modulate calcium ions homeodynamics and membrane potential in rat isolated cerebral vessels during vasomotion were simulated by a three-variable non-linear model, as well as in [19], where the performance of habitat suitability models for macrophytes was assessed in terms of temperature, acidity and conductivity
Summary
The availability of large datasets in the scientific literature calls for novel and efficient algorithms to build models that are potentially able to explain the complex non-linear relationships between attributes [1]. Several applications in sciences and engineering rely on inferring a relationship between a dependent variable z ∈ R and a pair of independent variables ( x, y) ∈ R2 on the basis of a number of joint observations of these three variables’ values. This kind of modeling is termed trivariate. Further illustrations are found in [18], where the effects of pharmacological interventions that modulate calcium ions homeodynamics and membrane potential in rat isolated cerebral vessels during vasomotion were simulated by a three-variable non-linear model, as well as in [19], where the performance of habitat suitability models for macrophytes was assessed in terms of temperature, acidity and conductivity
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
