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Modelling higher-order dependence with cumulants
Jason Morton
Penn State
Abstract:
Models and estimators for covariance matrices are very well studied. For non-Gaussian distributions, simply studying covariance gives an incomplete picture. Extending the Edgeworth series gives the pxpxp skewness tensor, the pxpxpxp kurtosis tensor, and so on. We describe a strategy for building multilinear factor models of cumulant tensors using subspace varieties. This leads to a difficult optimization problem and a fully implicit, gradient-based numerical optimization method using parallel transport on the Grassmannian to perform estimation. We also discuss some of the associated statistical challenges and applications.
Tuesday, May 10, 2011
11:00AM AP&M 2402
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Philip E. Gill
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Jason Morton
Penn State
Abstract:
Models and estimators for covariance matrices are very well studied. For non-Gaussian distributions, simply studying covariance gives an incomplete picture. Extending the Edgeworth series gives the pxpxp skewness tensor, the pxpxpxp kurtosis tensor, and so on. We describe a strategy for building multilinear factor models of cumulant tensors using subspace varieties. This leads to a difficult optimization problem and a fully implicit, gradient-based numerical optimization method using parallel transport on the Grassmannian to perform estimation. We also discuss some of the associated statistical challenges and applications.
Tuesday, May 10, 2011
11:00AM AP&M 2402