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Bayesian Computation of the Intrinsic Structure of Factor Analytic Models

並列摘要


The study of factor analytic models often has to address two important issues: (a) the determination of the ”optimum” number of factors and (b) the derivation of a unique simple structure whose interpretation is easy and straightforward. The classical approach deals with these two tasks separately, and sometimes resorts to ad-hoc methods. This paper proposes a Bayesian approach to these two important issues, and adapts ideas from stochastic geometry and Bayesian finite mixture modelling to construct an ergodic Markov chain having the posterior distribution of the complete collection of parameters (including the number of factors) as its equilibrium distribution. The proposed method uses an Automatic Relevance Determination (ARD) prior as the device of achieving the desired simple structure. A Gibbs sampler updating scheme is then combined with the simulation of a continuous-time birth-and-death point process to produce a sampling scheme that efficiently explores the posterior distribution of interest. The MCMC sample path obtained from the simulated posterior then provides a flexible ingredient for most of the inferential tasks of interest. Illustrations on both artificial and real tasks are provided, while major difficulties and challenges are discussed, along with ideas for future improvements.

被引用紀錄


葉綉燕(2008)。台北縣國民小學學務人員角色知覺、 工作壓力與自我效能之研究〔碩士論文,淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2008.01068
KatarzynaKorona(2013)。在臺歐洲學生華語學習動機與學習適應之研究〔碩士論文,國立臺北科技大學〕。華藝線上圖書館。https://doi.org/10.6841/NTUT.2013.00199

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