Abstract
Bimodality is observed in empirical distributions of variables related\r\nto materials (glass resistance), companies (productivity) and natural phenomena\r\n(geyser eruption). Our proposal for modeling bimodality exploits the change of\r\nvariables theorem requiring the choice of a generating density function which represents\r\nthe main features of the phenomena under analysis, and the choice of the\r\ntransforming function ϕ(x) that describes the observed departure from the expected\r\nbehaviour. The novelty of this work consists in putting attention to the choice of\r\nϕ(x) in two different cases: when bimodality arises from a slight departure from\r\nunimodality and when it is a proper structural feature of the variable under study.\r\nAs an example we use the R ”geyser” dataset.
| Original language | English |
|---|---|
| Title of host publication | SIS 2017 Statistics and Data Science: new challenges, new generations |
| Publisher | Firenze University Press |
| Pages | 417-422 |
| Number of pages | 6 |
| ISBN (Print) | 978-88-6453-521-0 |
| Publication status | Published - 2017 |
Keywords
- bimodal density function
- change of variables theorem
Fingerprint
Dive into the research topics of 'Change of Variables theorem to fit Bimodal Distributions'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver