On the Evolution of Surnames
AbstractThe problem of determining how family names evolve preoccupies both statistics and human biology. The determination of a proper and well justified probability model to describe the probability distribution of surnames has been confronted by many authors. In this paper two stochastic models giving rise to the Yule distribution are proposed to explain and fit some observed surname frequency distributions. The first is based on a contagion hypothesis in the sense that the more occurrences a surname has had the more likely it is to have a further occurrence. The second model is based on a weaker set of assumptions which also allows "immigration" of new surnames. The distribution that arises from these models is then fitted to actual data and the fit is compared to that provided by the discrete Pareto distribution
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 6255.
Date of creation: 1989
Date of revision:
Publication status: Published in International Statistical Review 2.57(1989): pp. 161-167
Discrete Pareto distribution; Surname distribution; Yule distribution;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Panaretos, John, 1989. "A Probability Model Involving the Use of the Zero-Truncated Yule Distribution for Analysing Surname Data," MPRA Paper 6254, University Library of Munich, Germany.
- De Luca, Andrea & Rossi, Paolo, 2009. "Renormalization group evaluation of exponents in family name distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3609-3614.
- Zornig, Peter & Altmann, Gabriel, 1995. "Unified representation of Zipf distributions," Computational Statistics & Data Analysis, Elsevier, vol. 19(4), pages 461-473, April.
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