TENSOR-VARIATE FINITE MIXTURE MODELING FOR THE ANALYSIS OF UNIVERSITY PROFESSOR REMUNERATION

• 成果类型: Article
• 署名作者: Sarkar, Shuchismita; Melnykov, Volodymyr; Zhu, Xuwen
• 署名单位: University System of Ohio; Bowling Green State University; University of Alabama System; University of Alabama Tuscaloosa
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1420
• 发表日期: 2021
• 页码: 1017-1036
• 关键词: faculty salaries; maximum-likelihood; algorithm; GENDER; equity; FAMILY

摘要

There has been a long-standing interest in the analysis of university professor salary data. The vast majority of the publications on the topic employ linear regression models in an attempt to predict individual salaries. Indeed, the administration of any academic institution is interested in adequately compensating the faculty to attract and keep the best specialists available on the market. However, higher administration and legislators are not concerned with the matter of individual compensation and need to have a bigger picture for developing university strategies and policies. This paper is the first attempt to model university compensation data at the institutional level. The analysis of university salary patterns is a challenging problem due to the heterogeneous, skewed, multiway and temporal nature of the data. This paper aims at addressing all the above-mentioned issues by proposing a novel tensor regression mixture model and applying it to the data set obtained from the American Association of University Professors. The utility of the developed model is illustrated on addressing several important questions related to gender equity and peer institution comparison.