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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Testing covariance matrices in high dimensions -
Danning Li (Jilin University)
DTSTART;TZID=Europe/London:20180227T110000
DTEND;TZID=Europe/London:20180227T120000
UID:TALK101548AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/101548
DESCRIPTION:Testing covariance structure is of significant int
erest in many areas of high-dimensional inference.
Using extreme-value form statistics to test again
st sparse alternatives and using quadratic form st
atistics to test against dense alternatives are tw
o important testing procedures for high-dimensiona
l independence. However\, quadratic form statistic
s suffer from low power against sparse alternative
s\, and extreme-value form statistics suffer from
low power against dense alternatives with small di
sturbances. It would be important and appealing to
derive powerful testing procedures against genera
l alternatives (either dense or sparse)\, which is
more realistic in real-world applications. Under
the ultra high-dimensional setting\, we propose tw
o novel testing procedures with explicit limiting
distributions to boost the power against general a
lternatives.
LOCATION:Seminar Room 2\, Newton Institute
CONTACT:INI IT
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