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Embedding nonnegative definite Toeplitz matrices in nonnegative definite circulant matrices, with application to covariance estimation

机译:在非负定循环矩阵中嵌入非负定Toeplitz矩阵,并将其应用于协方差估计

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The class of nonnegative definite Toeplitz matrices that can be embedded in nonnegative definite circulant matrices of a larger size is characterized. An equivalent characterization in terms of the spectrum of the underlying process is also presented, together with the corresponding extremal processes. It is shown that a given finite-duration sequence rho can be extended to be the covariance of a periodic stationary processes whenever the Toeplitz matrix R generated by this sequence is strictly positive definite. The sequence rho =1, cos alpha , cos 2 alpha with ( alpha / pi ) irrational, which has a unique nonperiodic extension as a covariance sequence, demonstrates that the strictness is needed. A simple constructive proof supplies a bound on the abovementioned period in terms of the minimal eigenvalue of R. It also yields, under the same conditions, an extension of rho to covariances that eventually decay to zero. For the maximum-likelihood estimate of the covariance of a stationary Gaussian process, the extension length required for using the estimate-maximize iterative algorithm is determined.
机译:可以嵌入较大尺寸的非负定循环矩阵的一类非负定Toeplitz矩阵。还介绍了在基础过程的频谱方面的等效特征以及相应的极端过程。结果表明,只要由该序列产生的Toeplitz矩阵R严格为正定的,就可以将给定的有限持续时间序列rho扩展为周期平稳过程的协方差。序列rho = 1,cos alpha,cos 2 alpha具有(alpha / pi)无理数,具有唯一的非周期扩展作为协方差序列,表明需要严格性。一个简单的构造性证明就R的最小特征值为上述周期提供了一个界限。在相同条件下,它还会产生rho扩展为协方差,最终降低到零。对于平稳高斯过程的协方差的最大似然估计,确定使用估计最大化迭代算法所需的扩展长度。

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