... Modeling. covariance. matrices. via. partial. autocorrelations. “We study the role of partial autocorrelations in the reparameteriza- tion and parsimonious modeling of a covariance matrix. The work is motivated by and tries to mimic the ...
Concentrating on the linear aspect of this subject, Time Series Analysis provides an accessible yet thorough introduction to the methods for modeling linear stochastic systems.
These are used to develop graphical and significance tests for different hypotheses involving one or more independent high-dimensional linear time series.
... modeling of several covariance matrices and some results on the propriety of the posterior for linear regression ... via partial autocorrelations. Journal of Multivariate Analysis 100, 2352–2363. Daniels, M. J. and R. E. Kass (1999) ...
... covariance matrices and dynamic models for longitudinal data. Biometrika, 89(3):553–566. Daniels, M. and Pourahmadi, M. (2009). Modeling covariance matrices via partial autocorrelations. Journal of Multivariate Analysis, 100(10):2352 ...
We discuss several practical methods for constructing probit autocorrelation-consistent standard errors, drawing on the generalized method of moments techniques of Hansen (1982), Newey-West (1987) and others, and we provide simulation ...
This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples.
This paper studies the estimation of dynamic covariance matrices with multiple conditioning variables, where the matrix size can be ultra large (divergent at an exponential rate of the sample size).
"In recent years, the problem of estimating a sparse inverse covariance matrix in the moderate-to-large dimensional setting has been an important and challenging task in many fields, including genomics, finance and earth sciences.
High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression ...