Modelling structured correlation matrices
提出用超球面坐标或角度重新参数化相关矩阵的Cholesky因子，确保估计的结构化相关矩阵正定，并通过最大似然估计角度，证明了估计量的一致性和渐近正态性。
Ensuring positive definiteness of an estimated structured correlation matrix is challenging. We show that reparameterizing Cholesky factors of correlation matrices using hyperspherical coordinates or angles provides a flexible and effective solution. Once a structured correlation matrix is identified, the corresponding angles and hence the constrained correlations may be estimated by maximum likelihood. Consistency and asymptotic normality of the maximum likelihood estimators of the angles are established. Examples demonstrate the flexibility of the method.