Bayesian Estimation of Linear Regression Model with Multiple Change Points for Missing Data
DOI:
https://doi.org/10.53555/ephms.v6i10.1736Keywords:
complete-data likelihood function, full conditional distribution; prior distribution, Gibbs sampling, Metropolis-Hastings algorithmAbstract
The missing data is filled in by a random way. The completedata likelihood function of linear regression model with multiple change points for missing data is obtained. The full conditional distributions of change-point positions and other unknown parameters are studied. All the parameters are sampled by Gibbs sampler, and the means of Gibbs samples are taken as Bayesian estimations of the parameters. Random simulation results show that the estimations are fairly accurate.
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