Wavelet-Based Bayesian Estimation of Partially Linear Regression Models with Long Memory Errors

摘要: In this paper we focus on partially linear regression models with long memory errors, and propose a wavelet-based Bayesian procedure that allows the simultaneous estimation of model parameters nonparametric part model. Employing discrete wavelet transforms is crucial in order to simplify dense variance-covariance matrix error. We achieve fully inference by adopting Metropolis algorithm within Gibbs sampler. evaluate performances proposed method simulated data. addition, present an application Northern hemisphere temperature data, benchmark literature.