Journal
STATISTICA SINICA
Volume 19, Issue 4, Pages 1463-1478Publisher
STATISTICA SINICA
Keywords
Bayesian inference; long memory; MCMC; partially linear regression model; wavelet transforms
Categories
Funding
- NSF [DMS-0605001]
- NIH [R01 HG003319-01]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0835552] Funding Source: National Science Foundation
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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 the model parameters and the nonparametric part of the model. Employing discrete wavelet transforms is crucial in order to simplify the dense variance-covariance matrix of the long memory error. We achieve a fully Bayesian inference by adopting a Metropolis algorithm within a Gibbs sampler. We evaluate the performances of the proposed method on simulated data. In addition, we present an application to Northern hemisphere temperature data, a benchmark in the long memory literature.
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