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                            Library & Multimedia
 Technical note: Evaluation of missing data imputation methods for human osteometric measurements
NCJ Number
              308005
Journal

*American Journal of Biological Anthropology* Volume: 181 Issue: 4 Dated: 2023 Pages: 666-676
Author(s)
          Jinyong Pang;
                        Xiaoming Liu
Date Published

2023
Length
              11 pages
Annotation
              This article provides a procedure for choosing appropriate imputation methods of incomplete bioarcheological or forensic skeleton specimen data, with an explanation of the authors’ research methodology and procedure development process.
Abstract
              It is not uncommon for biological anthropologists to analyze incomplete bioarcheological or forensic skeleton specimens. As many quantitative multivariate analyses cannot handle incomplete data, missing data imputation or estimation is a common preprocessing practice for such data. Using William W. Howells' Craniometric Data Set and the Goldman Osteometric Data Set, the authors evaluated the performance of multiple popular statistical methods for imputing missing metric measurements. Results indicated that multiple imputation methods outperformed single imputation methods, such as Bayesian principal component analysis (BPCA). Multiple imputation with Bayesian linear regression implemented in the R package norm2, the Expectation–Maximization (EM) with Bootstrapping algorithm implemented in Amelia, and the Predictive Mean Matching (PMM) method and several of the derivative linear regression models implemented in mice, performed well regarding accuracy, robustness, and speed. Based on the findings of this study, the authors suggest a practical procedure for choosing appropriate imputation methods. Publisher Abstract Provided
      
  
          Additional Details
Grant Number(s)
        15PNIJ-22-GG-04431-RESS


  
  
        
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        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Improve craniometric ancestry estimation with deep learning methods
    
  
  
Date Published: January 1, 2023
    
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