Maximum likelihood inference for mixtures of t factor analyzers with incomplete data

• Main Authors: Meng-Chih Liu, 劉孟智
• Other Authors: 林宗儀
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/p48a5u

碩士 === 國立中興大學 === 統計學研究所 === 106 === 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.