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UID:20250913T093504EDT-4106n75WRF@132.216.98.100
DTSTAMP:20250913T133504Z
DESCRIPTION:Aaron Fisher\, PhD Candidate\n\nPhD candidate in Biostatistics\
 , Johns Hopkins Bloomberg School of Public Health\n\nAn introduction to Pr
 incipal Component Analysis (PCA) for high dimensional data\, plus topics i
 n PCA consistency and fast bootstrap computations\n\nAbstract:\n	This casua
 l presentation will include an introduction to principal component analysi
 s (PCA) as a method for summarizing high dimensional (HD) data (e.g. brain
  images or genomic data). I will also survey a few results on conditions u
 nder which sample principal components (PCs) can have poor performance -- 
 specifically when they can diverge from the population PCs as dimension in
 creases. Finally\, I'll talk some about my own research on PCA\, which foc
 uses on fast computational methods for estimating standard errors of sampl
 e PCs. These methods are based around a bootstrap procedure\, and can redu
 ce computation time from days to minutes compared to standard bootstrap me
 thods.\n\nBio:\n\nhttp://aaronjfisher.github.io/\n\n \n
DTSTART:20160330T160000Z
DTEND:20160330T173000Z
LOCATION:Room 48\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Tutorial/Seminar: 'An introduction to Principal Component Analysis 
 (PCA) for high dimensional data\, plus topics in PCA consistency and fast 
 bootstrap computations'
URL:/epi-biostat-occh/channels/event/tutorialseminar-i
 ntroduction-principal-component-analysis-pca-high-dimensional-data-plus-to
 pics-pca-259807
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