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BEGIN:VEVENT
DTSTAMP:20260416T020345Z
DTSTART;VALUE=DATE-TIME:20120116T120000
DTEND;VALUE=DATE-TIME:20120116T130000
SUMMARY:CRiSM Seminar - Shinto Eguchi (Institute of Statistical Mathemati
 cs\, Japan)
TZID:Europe/London
UID:20120116-094d43ed33cac742013413a5d2640f7f@warwick.ac.uk
CREATED:20111206T135241Z
DESCRIPTION:Shinto Eguchi (Institute of Statistical Mathematics\, Japan) 
 Maximization of a generalized t-statistic for linear discrimination in t
 he two group classification problem We discuss a statistical method for 
 the classification problem with two groups labelled 0 and 1. We envisage
  a situation in which the conditional distribution given label 0 is well
  specified by a normal distribution\, but the conditional distribution g
 iven label 1 is not well modelled by any specific distribution. Typicall
 y in a case-control study the distribution in the control group can be a
 ssumed to be normal\, however the distribution in the case group may dep
 art from normality. In this situation the maximum t-statistic for linear
  discrimination\, or equivalently Fisher's linear discriminant function\
 , may not be optiimal. We propose a class of generalized t-statistics an
 d study asymptotic consistency and normality. The optimal generalized t-
 statistic in the sense of asymptotic variance is derived in a semi-param
 etric manner\, and its statistical performance is confirmed in several n
 umerical experiments.
LOCATION:C1.06
CATEGORIES:CRiSM Seminars,Seminars
LAST-MODIFIED:20111206T135241Z
ORGANIZER;CN=Paula Matthews:
END:VEVENT
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