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UID:20250712T093431EDT-0306Rvl6Zu@132.216.98.100
DTSTAMP:20250712T133431Z
DESCRIPTION:Title: Heteroskedastic Sparse PCA in High Dimensions\n\n\n	Abstr
 act:\n\n\nPrincipal component analysis (PCA) is one of the most commonly u
 sed techniques for dimension reduction and feature extraction. Though it h
 as been well-studied for high-dimensional sparse PCA\, little is known whe
 n the noise is heteroskedastic\, which turns out to be ubiquitous in many 
 scenarios\, like biological sequencing data and information network data. 
 We propose an iterative algorithm for sparse PCA in the presence of hetero
 skedastic noise\, which alternatively updates the estimates of the sparse 
 eigenvectors using the power method with adaptive thresholding in one step
 \, and imputes the diagonal values of the sample covariance matrix to redu
 ce the estimation bias due to heteroskedasticity in the other step. Our pr
 ocedure is computationally fast and provably optimal under the generalized
  spiked covariance model\, assuming the leading eigenvectors are sparse. A
  comprehensive simulation study demonstrates its robustness and effectiven
 ess in various settings.\n\n\n	Speaker\n\n\nDr. Zhao Ren is an Associate Pr
 ofessor of Statistics in Dietrich School of Arts and Sciences at the Unive
 rsity of Pittsburgh. His research focuses on high dimensional statistical 
 inference\, graphical models and statistical machine learning\, nonparamet
 ric function estimation\, and applications in Statistical Genomics.\n\n\n	
 \n		\n			Hybrid: In person / Zoom\n\n			Location: Burnside Hall 1104\n\n			https://mc
 gill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09\n\n			Meeting
  ID: 834 3668 6293\n\n			Passcode: 12345\n		\n	\n\n
DTSTART:20230310T203000Z
DTEND:20230310T213000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Zhao Ren (University of Pittsburgh)
URL:/mathstat/channels/event/zhao-ren-university-pitts
 burgh-346582
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