A non-iterative posterior sampling algorithm for Laplace linear regression model

作者：Yang, Fengkai; Yuan, Haijing^*

来源：Communications in Statistics - Simulation and Computation, 2017, 46(3): 2488-2503.

DOI：10.1080/03610918.2015.1056354

• EM algorithm
• Gibbs sampling
• Inverse Bayes formulae
• Laplace linear regression
• Sampling/important resampling

In this article, a non-iterative sampling algorithm is developed to obtain an independently and identically distributed samples approximately from the posterior distribution of parameters in Laplace linear regression model. By combining the inverse Bayes formulae, sampling/importance resampling, and expectation maximum algorithm, the algorithm eliminates the diagnosis of convergence in the iterative Gibbs sampling and the samples generated from it can be used for inferences immediately. Simulations are conducted to illustrate the robustness and effectiveness of the algorithm. Finally, real data are studied to show the usefulness of the proposed methodology.

• 出版日期2017
• 单位山东大学