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Nonparametric Identification of Latent Dimension under Heavy-Tailed and Mixed-Signals
Principal component analysis and factor analysis are foundational to the study of high-dimensional data, yet their efficacy depends entirely on correctly identifying the latent dimension $r$. While parallel analysis (PA) is widely regarded as the gold standard for this task, its performance suffers
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