Joint Central Limit Theorem for Eigenvalue Statistics from Several Dependent Large Dimensional Sample Covariance Matrices with Application

Let X _n = (x _{i j} ) be a k ×n data matrix with complex‐valued, independent and standardized entries satisfying a Lindeberg‐type moment condition. We consider simultaneously R sample covariance matrices , where the Q _r 's are non‐random real matrices with common dimensions p ×k (k ≥p ). Assuming that both the dimension p and the sample size n grow to infinity, the limiting distributions of the eigenvalues of the matrices { B _{n r} } are identified, and as the main result of the paper, we establish a joint central limit theorem (CLT) for linear spectral statistics of the R matrices { B _{n r} }. Next, this new CLT is applied to the problem of testing a high‐dimensional white noise in time series modelling. In experiments, the derived test has a controlled size and is significantly faster than the classical permutation test, although it does have lower power. This application highlights the necessity of such joint CLT in the presence of several dependent sample covariance matrices. In contrast, all the existing works on CLT for linear spectral statistics of large sample covariance matrices deal with a single sample covariance matrix (R = 1).     

Patterned matrix derivatives

A general procedure for obtaining matrix derivatives of functions of nonlinear patterned matrices is proposed. The method is extended to obtain the Jacobians of patterned matrix transformations. Nel (1980) and Wiens (1985) consider the linear patterned cases. The procedure proposed here takes care of these cases as well.     

ALL THE GAUSSIAN WHITE NOISE SERIAL COVARIANCE MOMENTS TO ORDER FOUR

Using a recursive method, we obtain all the cumulants, central moments, and moments about zero, up to order 4, for the mean-corrected serial covariances from series realisations of length n, given a Gaussian white noise process. Some implicit higher order results are also derived.     

Paired permutation testing in 2<Superscript><Emphasis Type="Italic">k</Emphasis></Superscript> unreplicated factorials

Exact permutation testing of effects in unreplicated two-level multifactorial designs is developed based on the notion of realigning observations and on paired permutations. This approach preserves the exchangeability of error components for testing up tok effects. Advantages and limitations of exact permutation procedures for unreplicated factorials are discussed and a simulation study on paired permutation testing is presented.     

Graphical Comparison of Covariance Matrices

Procedures for comparing within-group covariance matrices are developed, based on separate analyses of the variances and of the correlations. The variances and the correlations are represented as two two-way tables, with the columns representing groups. Graphical procedures based on comparisons of linear regressions are presented, by considering a multiplicative columns-regression model for the interaction of groups x variances and of groups x correlations. A multivariate comparison leads to the use of a generalized eigenanalysis to display any differences in covariance structure.     

On complete classes of experiments for certain invariant problems of linear inference

For certain non-singularly estimable full-rank appropriately invariant problems of linear inference in the setting of block designs, some complete classes of experiments have been characterized through the relation between the relevant C-matrices and their g-inverses (of the Moore-Penrose type) in regard to the specific invariance criterion discussed here. It follows that for a $\text{G}$-invariant non-singularly estimable full-rank problem, a complete class of experiments formally consists only of $\text{G}$-invariant designs.