
Determining Provenance from Compositional Data
Cambridge University Press
Will be published approx. on 31. January 2026
Book
Paperback/Softback
75 pages
978-1-009-63417-5 (ISBN)
Description
Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. However, when reviewing published research, which usually claims to have reached a high degree of confidence in the assignment of materials, the authors have detected that those applying these methods can make serious errors that compromise the inferences made. This Element reconsiders the use of statistical methods to address the problem of provenance analysis of archaeological materials using a step-by-step procedure that allows the recognition of natural groups in the data, thus obtaining better quality classifications while avoiding the problems of total or partial overlaps in the chemical groups (common in biplots). To evaluate the methods proposed here, the challenge of group search in ceramic materials is addressed using algorithms derived from model-based clustering. For cases with partial data labeling, a semi-supervised algorithm is applied to obsidian samples.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Product notice
Paperback (trade)
Illustrations
Worked examples or Exercises
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 6 mm
Weight
139 gr
ISBN-13
978-1-009-63417-5 (9781009634175)
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Schweitzer Classification
Other editions
Additional editions

Pedro A. Lopez-Garcia | Denisse L. Argote
Determining Provenance from Compositional Data
Book
approx. 01/2026
Cambridge University Press
€69.00
Not yet published
Persons
Author
National Institute of Anthropology and History, Mexico
National Institute of Anthropology and History, Mexico
Content
1. Introduction; 2. Sample size; 3. Imputation of missing values; 4. Data transformation; 5. Data diagnosis; 6. Dimensionality reduction; 7. Model validation; 8. Compositional study of archaeological pottery: example for variable selection; 9. Compositional study of obsidian materials: example of semi-supervised classification; 10. Final comments; References.