A superresolution method of ARMA spectral estimation

An Efficient Linear Method of Arma Spectral Estimation

A three step method for obtaining nearly maximum likelihood ARMA spectral estimates is presented. The computational complexity of the algorithm is comparable to YuleWalker methods, but the method gives asymptotically efficient estimates. The implementation of the algorithm is discussed, and numerical examples are presented to illustrate its performance.

A Spectral Estimator of Arma Parameters from Thresholded Data

We consider computationally-fast methods for estimating parameters in ARMA processes from binary time series data, obtained by thresholding the latent ARMA process. All methods involve matching estimated and expected autocorrelations of the binary series. In particular, we focus on the spectral representation of the likelihood of an ARMA process and derive a restricted form of this likelihood, ...

Estimation of latent Gaussian ARMA models for categorical behaviour data

We consider the tting of latent Gaussian models to categorical time series of cow feeding data. We derive a spectral quasi-likelihood for the data, and compare it with least squares ts to autocorrelations and MCMC estimators of the parameters in thresholded ARMA processes. We show that the spectral method is more e cient than least squares and far faster than MCMC.

Improved ARMA spectral estimation using the canonical variate method

Enforcing solvability of a nonlinear matrix equation and estimation of multivariate ARMA time series

The matrix equation X +AX−1AT = B, arising in parameter estimation of certain time series models, is solvable only for certain values of the matrices A,B. We present a numerical method to modify A,B in order to make the matrix equation solvable. Since solvability depends on the location of the eigenvalues of the palindromic matrix polynomial λA+ λB+A , our method works by moving those eigenvalu...