Please use this identifier to cite or link to this item:
標題: 具不完整資料的混合t因子分析器之最大概似推論
Maximum likelihood inference for mixtures of t factor analyzers with incomplete data
作者: 劉孟智
Meng-Chih Liu
關鍵字: 資料縮減;ECM 演算法;因子分析器;最大概似估計;多變量t分佈;遺失值;模型選擇;data reduction;ECM algorithms;factor analyzer;maximum likelihood estimation;multivariate t distribution;missing values;model selection

The mixtures of t factor analyzers (MtFA) is a powerful tool widely used for robust clustering of high-dimensional data in the presence of heavy-tailed noises. However, the occurrence of missing values may frequently cause analytical intractability and high computational complexity in the fitting of these models. In thesis, we aim at developing an expectation conditional maximization(ECM) algorithm with less data augmentation for fast maximum-likelihood (ML) estimation of MtFA with possibly missing values. For making likelihood-based inference, the missing data mechanism is considered to be missing at random (MAR). In addition, the score vector and empirical information matrix of the model are explicitly derived for large sample inference of estimated parameters. Practical issues related to the recovery of missing values and clustering of partially observed samples are also investigated. The practical utility of the proposed methodology is exemplified through the analysis of simulated and real data.
Rights: 同意授權瀏覽/列印電子全文服務,2021-08-20起公開。
Appears in Collections:統計學研究所

Files in This Item:
File SizeFormat Existing users please Login
nchu-107-7105018008-1.pdf1.09 MBAdobe PDFThis file is only available in the university internal network    Request a copy
Show full item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.