»ã±¨±êÌâ (Title)£ºHigh dimensional PCA(¸ßάÖ÷³É·Ö·ÖÎö)

»ã±¨ÈË (Speaker)£º Å˹âÃ÷ ½ÌÊÚ£¨Ëæ»ú¾ØÕóÁìÓò³ÛÃûר¼Ò£¬ÄÏÑóÀí¹¤´óѧ£©

»ã±¨¹¦·ò (Time)£º2021Äê11ÔÂ5ÈÕ(ÖÜÎå) 9£º00

»ã±¨µØÖ· (Place)£ºÌÚѶ»áÒ飨»áÒéºÅ£º958 938 143£©

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»ã±¨ÌáÒª£ºWe propose an approach based on sample eigenvalues of sample covariance matrices to estimate the number of significant components in high dimensional data. We show the consistency of the estimator in different type of data. Simulations are run to compare the performance with those existed approaches.