乐胖代购免代理版

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A compact form of the model is derived which facilitates the estimation procedure and two computational methods for the estimation are proposed: a Markov chain Monte Carlo algorithm and a scalable Bayesian EM algorithm. Being based on the spike-and-slab framework for fast posterior mode identification, the latter enables Bayesian data analysis of matrix-valued time series at large scales. The theoretical properties, comparative performance, and computational efficiency of the proposed model is investigated through simulated examples and an application to a panel of country economic indicators.<\/jats:p>","DOI":"10.1007\/s11222-024-10402-y","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T08:02:17Z","timestamp":1710144137000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Bayesian variable selection for matrix autoregressive models"],"prefix":"10.1007","volume":"34","author":[{"given":"Alessandro","family":"Celani","sequence":"first","affiliation":[]},{"given":"Paolo","family":"Pagnottoni","sequence":"additional","affiliation":[]},{"given":"Galin","family":"Jones","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,11]]},"reference":[{"key":"10402_CR1","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1002\/jae.2443","volume":"31","author":"D Ahelegbey","year":"2016","unstructured":"Ahelegbey, D., Billio, M., Casarin, R.: Bayesian graphical models for structural vector autoregressive processes. 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