A comprehensive comparison of algorithms for the statistical modelling of non-monotone relationships via isotonic regression of transformed data. (2019)

• Record Type: Journal Article
• Authors: Fiori, Simone
• Is Part Of: International journal of data analysis techniques and strategies. Volume 11:Number 1(2019)
• Page Start: 29
• Page End: 57
• Publication Date: 2019
• Subjects: non-monotone nonlinear data-fitting -- data transformation -- isotonic regression -- statistical regression; Electronic data processing -- Periodicals; Database searching -- Periodicals; 005
• Languages: English
• ISSNs: 1755-8050

Abstract

The paper treats the problem of nonlinear, non-monotonic regression of bivariate datasets by means of a statistical regression method known from the literature. In particular, the present paper introduces two new regression methods and illustrates the results of a comprehensive comparison of the performances of the best two previous methods, the two new methods introduced here and as much as ten standard regression methods known from the specialised literature. The comparison is performed over nine different datasets, ranging from electrocardiogram data to text analysis data, by means of four figures of merit, that include regression precision as well as runtime.