Department of Statistics
Texas A&M University
“Modeling Structured Correlation Matrices”
There has been a flurry of activity in the last two decades in reparametrizing the Cholesky factors of correlation matrices using hyperspherical coordinates where the ensuing angles are meaningful geometrically, but hard to interpret statistically. In spite of the lack of broadly accepted statistical interpretation, we demonstrate that these angles are quite flexible and effective for parsimonious modeling of large nearly block-structured correlation matrices commonly encountered in finance, environmental and biological sciences. Asymptotic normality of the maximum likelihood estimates of these angles as new parameters is established. Real examples will be used to demonstrate the flexibility and applicability of the methodology.
Joint work with Ruey Tsay, University of Chicago.