Description Usage Format Details Source References Examples
Simulated data
1 |
simcurve_smooth_normerr
: 50 by 100
simcurve_rough_normerr
: 50 by 100
simresp_normerr
: 50 by 1
tau_normerr
: 50 by 1
The simulated discretised curves are defined as x_i(t_j) = a_i cos(2t_j)+b_isin(4t_j)+c_i(t_j^2-π \times t_j+2/9π^2), where t represents the function support range and 0≤q t_1≤q t_2…≤q π are equispaced points within the function support range, a_i, b_i and c_i are independently drawn from a uniform distribution on [0,1], and n represents the sample size. For simulating a set of rough curves, we add one extra term d_j generated from U(-0.1, 0.1). Having defined functional curves, we then construct the regression mean function τ=10\times (a_i^2-b_i^2). Then the response variable is obtained by adding the regression mean function with a set of errors generated from a standard normal distribution
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.
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.
F. Ferraty, I. Van Keilegom, P. Vieu (2010) On the validity of the bootstrap in non-parametric functional regression, Scandinavian Journal of Statistics, 37(2), 286-306.
1 | data(simcurve_normerr)
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