»ã±¨±êÌâ (Title)£º»ùÓÚSpearmanÖÈÓйؾØÕóµÄ¸ßάÒò×Ó½¨Ä£ÖÐÒò×ÓÊýÁ¿µÄÎÈÖعÀ¼Æ

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»ã±¨¹¦·ò (Time)£º2024Äê09ÔÂ26ÈÕ (ÖÜËÄ) 11:00-12£º30

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»ã±¨ÌáÒª£ºDetermining 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¡¯s rank correlation matrix under the high-dimensional setting, where both dimension and sample size tend to infinity proportionally. Our estimator is applicable for scenarios where either the common factors or idiosyncratic errors follow heavy-tailed distributions. We prove that the proposed estimator is consistent under mild conditions. Numerical experiments also demonstrate the superiority of our estimator compared to existing methods, especially for the heavy-tailed case.