Optimal Bayesian Regression With Vector Autoregressive Data Dependency
Reihanian S. Dougherty E.R. Zollanvari A.
2024Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Signal Processing
2024#721854 - 1864 pp.
In this study, we derive a closed-form analytic representation of the optimal Bayesian regression when the data are generated from text{VAR}(p), which is a multidimensional vector autoregressive process of order p. Given the covariance matrix of the underlying Gaussian white-noise process, the developed regressor reduces to the conventional optimal regressor for a non-informative prior and setting p=0, which implies independent data. Our empirical results using both synthetic and real data show that the developed regressor can effectively be used in situations where the data are sequentially dependent.
Optimal Bayesian regression , serially dependent data , vector autoregressive processes
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Nazarbayev University, Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Astana, 010000, Kazakhstan
Texas A&M University, Center for Bioinformatics and Genomic Systems Engineering, Department of Electrical and Computer Engineering, College Station, 77843, TX, United States
Nazarbayev University
Texas A&M University
10 лет помогаем публиковать статьи Международный издатель
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