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SUMMARY:A powerful methodology for analyzing correlated high dimensional d
 ata with factor models - Hongyuan Cao (Florida State University)
DTSTART:20230609T143000Z
DTEND:20230609T160000Z
UID:TALK201949@talks.cam.ac.uk
CONTACT:97804
DESCRIPTION:Multiple testing under dependence is a fundamental problem in 
 high-dimensional statistical inference. We use a factor model to capture t
 he dependence. Existing literature with factor models imposes joint normal
 ity on the data or requires tuning parameters to obtain robust inference. 
 This paper looks at the problem differently by transposing approximate fac
 tor models. This allows heteroscedasticity and a more accurate estimation 
 of the covariance matrix of idiosyncratic errors by projections. We constr
 uct factor-adjusted one-sample and two-sample test statistics of high-dime
 nsional data. Extensive simulation studies demonstrate the favorable perfo
 rmance of the proposed method over state-of-the-art methods while controll
 ing the false discovery rate\, even for heavy-tailed data. The robustness 
 and tuning parameter-free features make the proposed method attractive to 
 practitioners.
LOCATION:MR1\, Centre for Mathematical Sciences\, Wilberforce Road\, Cambr
 idge
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