Estimation of a functional single index model with dependent errors and unknown error density

Han Lin Shang*

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 languageEnglish
Pages (from-to)3111-3133
Number of pages23
JournalCommunications in Statistics - Simulation and Computation
Volume49
Issue number12
Early online date28 Dec 2018
DOIs
Publication statusPublished - 1 Dec 2020
Externally publishedYes

Keywords

  • Error density estimation
  • Gaussian kernel mixture
  • Markov chain Monte Carlo
  • Nadaraya-Watson estimator
  • Scalar-on-function regression
  • Spectroscopy

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