Bayesian Estimation of Linear Regression Model with Multiple Change Points for Missing Data

Authors

  • Chaobing He School of Mathematics and Statistics, Anyang Normal University, Anyang 455000, China

DOI:

https://doi.org/10.53555/ephms.v6i10.1736

Keywords:

complete-data likelihood function, full conditional distribution; prior distribution, Gibbs sampling, Metropolis-Hastings algorithm

Abstract

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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Published

2020-10-30

How to Cite

He, C. . (2020). Bayesian Estimation of Linear Regression Model with Multiple Change Points for Missing Data. EPH - International Journal of Mathematics and Statistics, 6(10), 01–11. https://doi.org/10.53555/ephms.v6i10.1736