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SUMMARY:Estimation of Large Covariance Matrix - Professor Jianqing Fan (Pr
 inceton)
DTSTART:20080115T170000Z
DTEND:20080115T180000Z
UID:TALK8783@talks.cam.ac.uk
CONTACT:Helen Innes
DESCRIPTION:Large dimensionality comparable to the sample size is a common
  feature as in portfolio allocation\, risk management\, genetic network an
 d climatology.  In this talk\, we first use a multi-factor model to reduce
  the dimensionality and to estimate the covariance matrix for portfolio al
 location and risk management.  The impacts of dimensionality on the estima
 tion of covariance matrix and its inverse are examined.  We identify the s
 ituations under which the factor approach can gain substantially the perfo
 rmance and the cases where the gains are only marginal\, in comparison wit
 h the sample covariance matrix. Furthermore\, the impacts of the covarianc
 e matrix estimation on portfolio allocation and risk management are studie
 d.  In other class of problems such as genetic network or climatology\, sp
 arsity of the covariance matrix or its inverse arises natural.  We then es
 timate the large covariance matrix estimation by exploiting its sparsity u
 sing the penalized likelihood method.  Sampling property is established an
 d new algorithms are proposed.\n\n
LOCATION:Wolfson Room (MR 2) Centre for Mathematical Sciences\, Wilberforc
 e Road\, Cambridge
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