Statistics and Its Interface
Volume 15 (2022)
Bayesian estimation for partially linear varying coefficient spatial autoregressive models
Pages: 105 – 113
We propose a fully Bayesian estimation approach for partially linear varying coefficient spatial autoregressive models on the basis of B-spline approximations of nonparametric components. A computational efficient MCMC method that combines the Gibbs sampler with Metropolis–Hastings algorithm is implemented to simultaneously obtain the Bayesian estimates of unknown parameters, as well as their standard error estimates. Monte Carlo simulations are used to investigate the finite sample performance of the proposed method. Finally, a real data analysis of Boston housing data is used to illustrate the usefulness of the proposed methodology.
spatial autoregressive models, partially linear varying coefficient models, Bayesian estimate, Gibbs sampler, B-spline
2010 Mathematics Subject Classification
Primary 62F15. Secondary 62G05.
Tian’s work was supported by the National Natural Science Foundation of China (11801514), and by the Startup Foundation for Talents at Hangzhou Normal University(2019QDL039).
Xu’s work was supported by the Key Projects of statistical research in Zhejiang Province (20TJZZ13).
Du’s work was supported by the National Natural Science Foundation of China (11971045,11771032), the Natural Science Foundation of Beijing Municipality (1202001), and the Science and Technology Project of Beijing Municipal Education Commission (KM201910005015, KM202010005026).
Received 9 July 2020
Accepted 12 May 2021
Published 11 August 2021