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New statistical methods for the comparison and characterisation of particle shape

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journal contribution
posted on 2019-05-20, 14:27 authored by David GrahamDavid Graham, Richard J. Gadsden
This paper presents novel methods for robust statistical testing of particle shape data. Shape (the relative lengths of three orthogonal axes) is a key property of sedimentary particles, providing information on provenance, transport history and depositional environment. However, the usefulness of shape data, including the ability to make robust comparisons between samples, has been constrained by the absence of a satisfactory definition of the mean shape for a sample of particles. Such a definition is proposed and used to develop confidence regions for the population mean shape using both parametric (theoretical) and computational (bootstrap) methods. These techniques are based on a transform that permits multivariate statistical methods for the analysis of compositional data to be extended to shape. These techniques are validated with reference to a dataset of 169 clast samples and found to perform well. A statistical test on the mean – using the multivariate extension of Student's t‐test, Hotelling's T2 – is presented. The benefits of the methods presented are demonstrated with reference to a case study.

History

School

  • Social Sciences

Department

  • Geography and Environment

Published in

Earth Surface Processes and Landforms

Volume

44

Issue

12

Pages

2396 - 2407

Citation

GRAHAM, D.J. and GADSDEN, R.J., 2019. New statistical methods for the comparison and characterisation of particle shape. Earth Surface Processes and Landforms, 44 (12), pp.2396-2407.

Publisher

© Wiley

Version

  • AM (Accepted Manuscript)

Publisher statement

This is the peer reviewed version of the following article: GRAHAM, D.J. and GADSDEN, R.J., 2019. New statistical methods for the comparison and characterisation of particle shape. Earth Surface Processes and Landforms, 44 (12), pp.2396-2407, which has been published in final form at https://doi.org/10.1002/esp.4669. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

Acceptance date

2019-03-25

Publication date

2019-05-20

ISSN

0197-9337

eISSN

1096-9837

Language

  • en