... 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 ...
These are used to develop graphical and significance tests for different hypotheses involving one or more independent high-dimensional linear time series.
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).
... 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) ...
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.
Literature Review from the year 2020 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, , language: English, abstract: This paper is a review to the GARCH family’s models.
... 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 ...
With a focus on analyzing and modeling linear dynamic systems using statistical methods, Time Series Analysis formulates various linear models, discusses their theoretical characteristics, and explores the connections among stochastic ...
... matrices in hierarchical models. JAm Stat Assoc, 94, 1254–1263. Daniels M, Pourahmadi M. (2002). Dynamic models and ... Partial autocorrelation function of a nonstationary time series. J Multivariate Anal, 89, 135–147. Dempster A ...