Prove f b in (7.6) maximizes the log likelihood (7.5) by minimizing the negative of the log likelihood Lln |f| + L tr {ffb −1}

in the form L

X i

λi − ln λi − 1

+ Lp + Lln |f b|, where the λi values correspond to the eigenvalues in a simultaneous diagonalization of the matrices f and ˆf; i.e., there exists a matrix P such that P∗fP = I and P∗fPb = diag (λ1, . . . , λp) = Λ. Note, λi − ln λi − 1 ≥ 0 with equality if and only if λi = 1, implying Λ = I maximizes the log likelihood and f = f b is the maximizing value.

Section 7.3