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Skew-normal factor analysis models with incomplete data

机译:数据不完整的偏正态因素分析模型

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Traditional factor analysis (FA) rests on the assumption of multivariate normality. However, in some practical situations, the data do not meet this assumption; thus, the statistical inference made from such data may be misleading. This paper aims at providing some new tools for the skew-normal (SN) FA model when missing values occur in the data. In such a model, the latent factors are assumed to follow a restricted version of multivariate SN distribution with additional shape parameters for accommodating skewness. We develop an analytically feasible expectation conditional maximization algorithm for carrying out parameter estimation and imputation of missing values under missing at random mechanisms. The practical utility of the proposed methodology is illustrated with two real data examples and the results are compared with those obtained from the traditional FA counterparts.
机译:传统因素分析(FA)基于多元正态性的假设。但是,在某些实际情况下,数据不符合此假设。因此,从此类数据得出的统计推断可能会产生误导。本文旨在为数据中出现缺失值时的偏正态(SN)FA模型提供一些新工具。在这样的模型中,假定潜在因子遵循多变量SN分布的受限版本,并带有额外的形状参数以适应偏度。我们开发了一种分析可行的期望条件最大化算法,用于在随机机制缺失的情况下进行参数估计和缺失值的估算。通过两个真实的数据示例说明了所提出方法的实际实用性,并将结果与​​从传统FA同行那里获得的结果进行了比较。

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