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Michael Kenyhercz

Dados Biográficos

ID2769680
NOMEMichael Kenyhercz
PRENOMESMichael
SOBRENOMEKenyhercz
ASSINATURAKENYHERCZ M
VERIFICADONão
TOTAL DE OBRAS2
TOTAL DE CITAÇÕES1
TOTAL COMO AUTOR2
TOTAL COMO EDITOR0
PRIMEIRO ANO DE PUBLICAÇÃO2019
ANO MAIS RECENTE DE PUBLICAÇÃO2021
ÍNDICE H1
  • A New Theoretical Approach to Ancestry Estimation as Applied to Human Crania

    Michael Kenyhercz, Michael W Kenyhercz et al.•ARTICLE•Human Biology•2021•Referências: 2

    Since Frank Livingstone proposed the idea that there are no races, only clines, in 1962, little has changed in how anthropologists study and, ultimately, estimate ancestry. How we talk about the study of human variation may have changed—shifting away from “racial” labels and toward those of supposed ancestral origins—but the methods we use to label and analyze groups, however termed, have remained the same. The author suggests a new theoretical a…

  • Missing Data Imputation Using Morphoscopic Traits and Their Performance in the Estimation of Ancestry

    Michael Kenyhercz, Michael W Kenyhercz et al.•ARTICLE•Forensic Anthropology•2019•Citada por: 1

    Missing data are an inherent problem in biological anthropology for both reference data sets and individual cases. The goal of data imputation for forensic anthropological applications is to accurately estimate missing values by using other, observed values. To quantify the accuracy of macromorphoscopic data in conditions with slight (10%), moderate (25%), and severe (50%, 75%, and 90%) amounts of missing data, we selected four data-imputation te…

  • Missing Data Imputation Using Morphoscopic Traits and Their Performance in the Estimation of Ancestry

    Michael Kenyhercz, Michael W Kenyhercz et al.•ARTICLE•Forensic Anthropology•2019•Citada por: 1

    Missing data are an inherent problem in biological anthropology for both reference data sets and individual cases. The goal of data imputation for forensic anthropological applications is to accurately estimate missing values by using other, observed values. To quantify the accuracy of macromorphoscopic data in conditions with slight (10%), moderate (25%), and severe (50%, 75%, and 90%) amounts of missing data, we selected four data-imputation te…

  • Missing Data Imputation Using Morphoscopic Traits and Their Performance in the Estimation of Ancestry

    Michael Kenyhercz, Michael W Kenyhercz et al.•ARTICLE•Forensic Anthropology•2019•Citada por: 1

    Missing data are an inherent problem in biological anthropology for both reference data sets and individual cases. The goal of data imputation for forensic anthropological applications is to accurately estimate missing values by using other, observed values. To quantify the accuracy of macromorphoscopic data in conditions with slight (10%), moderate (25%), and severe (50%, 75%, and 90%) amounts of missing data, we selected four data-imputation te…

  • A New Theoretical Approach to Ancestry Estimation as Applied to Human Crania

    Michael Kenyhercz, Michael W Kenyhercz et al.•ARTICLE•Human Biology•2021•Referências: 2

    Since Frank Livingstone proposed the idea that there are no races, only clines, in 1962, little has changed in how anthropologists study and, ultimately, estimate ancestry. How we talk about the study of human variation may have changed—shifting away from “racial” labels and toward those of supposed ancestral origins—but the methods we use to label and analyze groups, however termed, have remained the same. The author suggests a new theoretical a…

Artificial Intelligence (2 obras) · Computer Science (2 obras) · Data set (2 obras) · Forensic Anthropology and Bioarchaeology Studies (2 obras) · Mathematics (2 obras) · Race, Genetics, and Society (2 obras) · Statistics (2 obras) · Archaeology (1 obras) · Artificial Intelligence (1 obras) · Biology (1 obras)

Ethnos_APP • Projeto Open Source • Licença MIT • Frontend v2.0.0 • Privacidade e Cookies • Documentação da API: api.ethnos.app/docs • Código da API: GitHub • DOI: 10.5281/zenodo.17049435 • Código do Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae