Posterior convergence for Bayesian functional linear regression
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 27-41 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 150 |
Publication status | Published - 2016 |
Externally published | Yes |
Link(s)
Abstract
We consider the asymptotic properties of Bayesian functional linear regression models where the response is a scalar and the predictor is a random function. Functional linear regression models have been routinely applied to many functional data analytic tasks in practice, and recent developments have been made in theory and methods. However, few works have investigated the frequentist convergence property of the posterior distribution of the Bayesian functional linear regression model. In this paper, we attempt to conduct a theoretical study to understand the posterior contraction rate in the Bayesian functional linear regression. It is shown that an appropriately chosen prior leads to the minimax rate in prediction risk.
Research Area(s)
- Functional regression, Minimax rate, Posterior contraction rate, Prediction risk, Reproducing kernel Hilbert space
Citation Format(s)
Posterior convergence for Bayesian functional linear regression. / Lian, Heng; Choi, Taeryon; Meng, Jie et al.
In: Journal of Multivariate Analysis, Vol. 150, 2016, p. 27-41.
In: Journal of Multivariate Analysis, Vol. 150, 2016, p. 27-41.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review