Abstract
The problem of error density estimation for a functional single index model with dependent errors is studied. A Bayesian method is utilized to simultaneously estimate the bandwidths in the kernel-form error density and regression function, under an autoregressive error structure. For estimating both the regression function and error density, empirical studies show that the functional single index model gives improved estimation and prediction accuracies than any nonparametric functional regression considered. Furthermore, estimation of error density facilitates the construction of prediction interval for the response variable.
Original language | English |
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Pages (from-to) | 3111-3133 |
Number of pages | 23 |
Journal | Communications in Statistics - Simulation and Computation |
Volume | 49 |
Issue number | 12 |
Early online date | 28 Dec 2018 |
DOIs | |
Publication status | Published - 1 Dec 2020 |
Externally published | Yes |
Keywords
- Error density estimation
- Gaussian kernel mixture
- Markov chain Monte Carlo
- Nadaraya-Watson estimator
- Scalar-on-function regression
- Spectroscopy