LOG-RATIO COMPOSITIONAL DATA ANALYSIS IN ARCHAEOMETRY*

By M. Baxter, I. Freestone. 2006

123 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Geophysics, Paleontology

View Paper: https://www.semanticscholar.org/paper/b46d9f444ee886cebaa59a8e50994dff957f0a0b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Janice L. Scealy
2. Alan H. Welsh
3. Eric C. Grunsky
4. Simon Preston
5. Andrew T. A. Wood
6. Michail Tsagris
7. Ian L. Dryden

In Geophysics

1. Simon Preston
2. Eric C. Grunsky
3. Andrew T. A. Wood
4. Michail Tsagris

In Paleontology

1. Simon Preston
2. Andrew T. A. Wood
3. Michail Tsagris

Citations in the corpus

• A data-based power transformation for compositional data (2011, methodology): “e for instance Hongite, Kongite and Boxite data analysed by Aitchison (1986). However it is not necessarily clear whether for data with different distributions that the LRA is still the best approach. Baxter and Freestone (2006) performed principal component analysis on both simulated and real data. ”
• Robust Principal Component Analysis for Power Transformed Compositional Data (2015, background): “When μ is not near p−1/21p, PCA based on wclr can be dominated by the smaller components in u which often have high relative variation and this can obscure structure (Baxter et al. 2005; Baxter and Freestone 2006).”