... 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 ...

... 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 ...

... Modeling multivariate distributions using copulas: applications in marketing, (with discussion).Marketing Science, 30, 4–21. [19] Daniels, M. and Pourahmadi, M. (2009). Modeling covariance matrices via partial autocorrelations. Journal ...

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.

This thesis focuses on the problem of estimating parameters in bilinear and trilinear regression models in which random errors are normally distributed.

These are used to develop graphical and significance tests for different hypotheses involving one or more independent high-dimensional linear time series.

This book consists of three parts: Part One is composed of two introductory chapters.

This book presents covariance matrix estimation and related aspects of random matrix theory.

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 ...