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

Simone Fiori

International Journal of Data Analysis Techniques and Strategies, 2019, vol. 11, issue 1, 29-57

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.

Keywords: non-monotone nonlinear data-fitting; data transformation; isotonic regression; statistical regression. (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=96617 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:11:y:2019:i:1:p:29-57

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().