Estimating multivariate latent-structure models
提出一种基于多线性分解的识别方法，通过联合对角化矩阵来识别有限混合模型和隐马尔可夫模型等多元潜结构，并给出估计量的分布理论和渐近性质。
A constructive proof of identification of multilinear decompositions of multiway arrays is presented. It can be applied to show identification in a variety of multivariate latent structures. Examples are finite-mixture models and hidden Markov models. The key step to show identification is the joint diagonalization of a set of matrices in the same nonorthogonal basis. An estimator of the latent-structure model may then be based on a sample version of this joint-diagonalization problem. Algorithms are available for computation and we derive distribution theory. We further develop asymptotic theory for orthogonal-series estimators of component densities in mixture models and emission densities in hidden Markov models.