  Determining the number of factors in high-dimensional factor modeling is
essential but challenging, especially when the data are heavy-tailed. In this
paper, we introduce a new estimator based on the spectral properties of
Spearman sample correlation matrix under the high-dimensional setting, where
both dimension and sample size tend to infinity proportionally. Our estimator
is robust against heavy tails in either the common factors or idiosyncratic
errors. The consistency of our estimator is established under mild conditions.
Numerical experiments demonstrate the superiority of our estimator compared to
existing methods.
