Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications

@article{Chen2019StructuredLF,
  title={Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications},
  author={Yunxiao Chen and Xiaoou Li and Siliang Zhang},
  journal={Journal of the American Statistical Association},
  year={2019},
  volume={115},
  pages={1756 - 1770}
}
Abstract–Latent factor models are widely used to measure unobserved latent traits in social and behavioral sciences, including psychology, education, and marketing. When used in a confirmatory manner, design information is incorporated as zero constraints on corresponding parameters, yielding structured (confirmatory) latent factor models. In this article, we study how such design information affects the identifiability and the estimation of a structured latent factor model. Insights are gained… 

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