Visualising data distributions with kernel density estimation and reduced chi-squared statistic
| dc.contributor.author | Spencer, Christopher | |
| dc.contributor.author | Yakymchuk, C. | |
| dc.contributor.author | Ghaznavi, M. | |
| dc.date.accessioned | 2017-10-30T08:16:36Z | |
| dc.date.available | 2017-10-30T08:16:36Z | |
| dc.date.created | 2017-10-30T08:03:06Z | |
| dc.date.issued | 2017 | |
| dc.identifier.citation | Spencer, C. and Yakymchuk, C. and Ghaznavi, M. 2017. Visualising data distributions with kernel density estimation and reduced chi-squared statistic. Geoscience Frontiers. | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/57327 | |
| dc.identifier.doi | 10.1016/j.gsf.2017.05.002 | |
| dc.description.abstract |
© 2017 China University of Geosciences (Beijing) and Peking University. The application of frequency distribution statistics to data provides objective means to assess the nature of the data distribution and viability of numerical models that are used to visualize and interpret data. Two commonly used tools are the kernel density estimation and reduced chi-squared statistic used in combination with a weighted mean. Due to the wide applicability of these tools, we present a Java-based computer application called KD. X to facilitate the visualization of data and the utilization of these numerical tools. | |
| dc.publisher | Elsevier | |
| dc.title | Visualising data distributions with kernel density estimation and reduced chi-squared statistic | |
| dc.type | Journal Article | |
| dcterms.source.issn | 1674-9871 | |
| dcterms.source.title | Geoscience Frontiers | |
| curtin.department | Department of Applied Geology | |
| curtin.accessStatus | Open access via publisher |
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