Mixtures of t factor analysers with censored responses and external covariates: An application to educational data from Peru

Wan Lun Wang, Luis M. Castro, Huei Jyun Li, Tsung I. Lin

研究成果: Article同行評審

摘要

Analysing data from educational tests allows governments to make decisions for improving the quality of life of individuals in a society. One of the key responsibilities of statisticians is to develop models that provide decision-makers with pertinent information about the latent process that educational tests seek to represent. Mixtures of (Formula presented.) factor analysers (MtFA) have emerged as a powerful device for model-based clustering and classification of high-dimensional data containing one or several groups of observations with fatter tails or anomalous outliers. This paper considers an extension of MtFA for robust clustering of censored data, referred to as the MtFAC model, by incorporating external covariates. The enhanced flexibility of including covariates in MtFAC enables cluster-specific multivariate regression analysis of dependent variables with censored responses arising from upper and/or lower detection limits of experimental equipment. An alternating expectation conditional maximization (AECM) algorithm is developed for maximum likelihood estimation of the proposed model. Two simulation experiments are conducted to examine the effectiveness of the techniques presented. Furthermore, the proposed methodology is applied to Peruvian data from the 2007 Early Grade Reading Assessment, and the results obtained from the analysis provide new insights regarding the reading skills of Peruvian students.

原文English
頁(從 - 到)316-336
頁數21
期刊British Journal of Mathematical and Statistical Psychology
77
發行號2
DOIs
出版狀態Published - 2024 5月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 藝術與人文(雜項)
  • 一般心理學

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