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                            Library & Multimedia
 Mixed cumulative probit: a multivariate generalization of transition analysis that accommodates variation in the shape, spread and structure of data
NCJ Number
              306263

Author(s)
          Kyra E. Stull;
                        Elaine Y. Chu;
                        Louise K. Corron;
                        Michael H. Price
Date Published

March 2023
Annotation
              This article presents research into resolving common issues where incomplete biological data is presented in forensic samples.
Abstract
              Biological data are frequently nonlinear, heteroscedastic and conditionally dependent, and often researchers deal with missing data. To account for characteristics common in biological data in one algorithm, the authors developed the mixed cumulative probit (MCP), a novel latent trait model that is a formal generalization of the cumulative probit model usually used in transition analysis. Specifically, the MCP accommodates heteroscedasticity, mixtures of ordinal and continuous variables, missing values, conditional dependence and alternative specifications of the mean response and noise response. Cross-validation selects the best model parameters (mean response and the noise response for simple models, as well as conditional dependence for multivariate models), and the Kullback–Leibler divergence evaluates information gain during posterior inference to quantify mis-specified models (conditionally dependent versus conditionally independent). Two continuous and four ordinal skeletal and dental variables collected from 1296 individuals (aged birth to 22 years) from the Subadult Virtual Anthropology Database are used to introduce and demonstrate the algorithm. In addition to describing the features of the MCP, the authors provide material to help fit novel datasets using the MCP. The flexible, general formulation with model selection provides a process to robustly identify the modelling assumptions that are best suited for the data at hand. (Published abstract provided)
      
  
          Additional Details
Grant Number(s)
        2015-DN-BX-K409
                                2017-DN-BX-0144


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Development of Modern Subadult Standards: Improved Age and Sex Estimation in U.S. Forensic Practice
                                Quantifying the Impact of Intrinsic and Extrinsic Factors to Improve Juvenile Age Estimation
    
  
  
Date Published: March 1, 2023
    
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