Xvar:
Simulated real-valued predictors in the semi-functional partial linear model
Description
Simulated real-valued predictors in the semi-functional partial linear model. It is a 50 by 2 matrix, where the column variables are generated from $U[0,1]$ and the true regression coefficients are (-1,2). Note that the estimation of the regression coefficient for these predictors depends crucially on the bandwidth parameter estimated in the functional Nadaraya-Watson estimator of the regression function
Source
H. L. Shang (2013) Bayesian bandwidth estimation for a semi-functional partial linear regression model with unknown error density, Computational Statistics, in press.References
H. L. Shang (2013) Bayesian bandwidth estimation for a semi-functional partial linear regression model with unknown error density, Computational Statistics, in press.H. L. Shang (2013) Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density, Computational Statistics and Data Analysis, 67, 185-198.
Examples
Run this codedata(Xvar)
data(tau_semierr)
data(simresp_semi_normerr)
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