Hashemi Farzane, Naderi Mehrdad, Jamalizadeh Ahad, Lin Tsung-I
Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.
Department of Statistics, Faculty of Natural & Agricultural Sciences, University of Pretoria, Pretoria, South Africa.
J Appl Stat. 2020 Jan 6;47(16):3007-3029. doi: 10.1080/02664763.2019.1709054. eCollection 2020.
This paper presents a robust extension of factor analysis model by assuming the multivariate normal mean-variance mixture of Birnbaum-Saunders distribution for the unobservable factors and errors. A computationally analytical EM-based algorithm is developed to find maximum likelihood estimates of the parameters. The asymptotic standard errors of parameter estimates are derived under an information-based paradigm. Numerical merits of the proposed methodology are illustrated using both simulated and real datasets.
本文通过假设不可观测因子和误差服从Birnbaum-Saunders分布的多元正态均值-方差混合，提出了因子分析模型的一种稳健扩展。开发了一种基于计算分析的期望最大化（EM）算法来寻找参数的最大似然估计。在基于信息的范式下推导了参数估计的渐近标准误差。使用模拟数据集和真实数据集说明了所提出方法的数值优点。
