Identifiable and interpretable nonparametric factor analysis
可识别且可解释的非参数因子分析
Factor models are widely used to reduce dimensionality in modeling high-dimensional data. However, there remains a need for models that can be reliably fit in modest sample sizes and are identifiable, interpretable, and flexible. To address this gap, we propose a NIFTY model that uses a linear factor structure with Gaussian residuals, but with a novel latent variable modeling structure. In particular, we model each latent variable as a one-dimensional nonlinear mapping of a…