Statistical Inference for Noisy Vector Autoregression

The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2020). "Statistical inference for high-dimensional vector autoregression with measurement error", arXiv preprint .


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1.0.1 by Xiang Lyu, a year ago

Browse source code at

Authors: Xiang Lyu [aut, cre] , Jian Kang [aut] , Lexin Li [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports lpSolve, abind

Suggests knitr, rmarkdown

See at CRAN