Mixtures of the normal mean-variance of Lindley factor analysis model with missing data | ||
| Journal of Statistical Modelling: Theory and Applications | ||
| دوره 4، شماره 2، مهر 2023، صفحه 35-56 اصل مقاله (397 K) | ||
| نوع مقاله: Original Scientific Paper | ||
| شناسه دیجیتال (DOI): 10.22034/jsmta.2024.21207.1128 | ||
| نویسندگان | ||
| Maryam Darijani1؛ Hojatollah Zakerzadeh* 1؛ Farzane Hashemi2 | ||
| 1Department of Statistics, Yazd University ,Yazd, Iran | ||
| 2Department of Statistics, University of Kashan, Kashan | ||
| چکیده | ||
| For a heterogeneous community comprised of multiple sub-communities, the model-based clustering method stands out as a suitable recommendation. Moreover, incomplete data collection and information loss may occur due to a variety of causes. In this paper, our focus is on investigating the mixture of factor analysis model in the presence of missing data. Here, the latent factors and errors within each sub-cluster exhibit non-normal characteristics and adhere to the normal mean-variance mixture of Lindley distribution. This model is termed the mixture of normal mean-variance mixture of Lindley factor analysis. To estimate model parameters and generate a single imputation of potential missing values under the missing with random mechanism, we introduce a generalized expectation-maximization algorithm. The number of factors and mixture components are determined by the evaluation criteria of the model. The proposed model's advantage is validated through a real dataset and simulation studies, demonstrating its superior performance compared to existing models. | ||
| کلیدواژهها | ||
| Asymmetry؛ ECME algorithm؛ Factor analysis model؛ Incomplete data؛ Mixture model؛ Model-based clustering | ||
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آمار تعداد مشاهده مقاله: 163 تعداد دریافت فایل اصل مقاله: 88 |
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