Joint Bayesian Modeling of Binomial and Rank Data for Primate Cognition

• 成果类型: Article
• 署名作者: Barney, Bradley J.; Amici, Federica; Aureli, Filippo; Call, Josep; Johnson, Valen E.
• 署名单位: University System of Georgia; Kennesaw State University; Max Planck Society; Universidad Veracruzana; Liverpool John Moores University; University of St Andrews; Max Planck Society
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1016223
• 发表日期: 2015
• 页码: 573-582
• 关键词: latent variable models; intelligence; binary

摘要

In recent years, substantial effort has been devoted to methods for analyzing data containing mixed response types, but such techniques typically do not include rank data among the response types. Some unique challenges exist in analyzing rank data, particularly when ties are prevalent. We present techniques for jointly modeling binomial and rank data using Bayesian latent variable models. We apply these techniques to compare the cognitive abilities of nonhuman primates based on their performance on 17 cognitive tasks scored on either a rank or binomial scale. To jointly model the rank and binomial responses, we assume that responses are implicitly determined by latent cognitive abilities. We then model the latent variables using random effects models, with identifying restrictions chosen to promote parsimonious prior specification and model inferences. Results from the primate cognitive data are presented to illustrate the methodology. Our results suggest that the ordering of the cognitive abilities of species varies significantly across tasks, suggesting a partially independent evolution of cognitive abilities in primates. Supplementary materials for this article are available online.