Selected article for: "different prior and prior information"
Author: Ziyue Liu; Wensheng Guo
Title: Government Responses Matter: Predicting Covid-19 cases in US under an empirical Bayesian time series framework
  • Document date: 2020_3_30
    • ID: boa0n9dz_5
    Snippet: We propose an empirical Bayesian time series framework to forecast the US trajectory by Based on the estimated parameters using the eight countries, our next task is forecast the US cases while incorporate one of the countries as the prior information. This is done through constructing conditional state space model from the functional mixed effects model conditional on the observed data of the specified country 13 . By running the Kalman filter f.....
    Document: We propose an empirical Bayesian time series framework to forecast the US trajectory by Based on the estimated parameters using the eight countries, our next task is forecast the US cases while incorporate one of the countries as the prior information. This is done through constructing conditional state space model from the functional mixed effects model conditional on the observed data of the specified country 13 . By running the Kalman filter forward on the conditional state space model with the US time series data and into the future, the results are the posterior prediction incorporating both the prior information from the specific country and the observed US data. As the reference country is only specified as the prior, the posterior can be substantially different from the prior, suggesting strong deviation from the reference country.

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