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Recognizing species diversity among large-bodied hominoids: a simulation test using missing data finite mixture
1Department of Anthropology, University of Tennessee, 250 S. Stadium Hall, Knoxville, Tennessee 37996-0720, USA. akramer@utk.edu
Journal of Human Evolution
|April 20, 1999
Summary
Finite mixture analysis effectively distinguishes species in fossil hominoid samples, even with incomplete data. This method accurately clusters individuals, outperforming traditional techniques like k-means for paleoanthropology.
Area of Science:
- Paleoanthropology
- Statistical analysis
- Evolutionary biology
Background:
- Distinguishing intra- vs. inter-specific differences in fossil samples is challenging.
- Fragmentary fossil remains limit the application of traditional multivariate analyses.
Purpose of the Study:
- To evaluate the efficacy of finite mixture analysis for species identification in hominoid fossils.
- To test the method's robustness with simulated incomplete fossil samples and missing data.
Main Methods:
- Finite mixture analysis applied to large-bodied hominoid samples.
- Simulated fragmentation by stochastically removing specimens and measurements.
- Comparison with k-means clustering for accuracy and missing data handling.
Main Results:
- Finite mixture analysis demonstrated high accuracy in clustering individuals by species, even with small sample sizes and missing data.
- Accuracy remained high (98%) even when sample size was reduced to 16 with missing data.
- Finite mixture analysis outperformed k-means, particularly in its ability to handle missing data.
Conclusions:
- Finite mixture analysis is a robust and accurate method for species identification in paleoanthropology.
- The method is particularly valuable for analyzing incomplete fossil records.
- A novel test for species identification using finite mixture analysis is proposed.