parameter_estimate_boot: Hypothesis testing via bootstrap

In BIG-S2/GFPLVCM: Genealized functional partinal linear varying-coefficient models

Description

Test the nullity of both the scalar and the functional covariates.

Usage

parameter_estimate_boot(y, x, z, lambda_s, lambda_u, bd, order_1, order_2, breaks, boot_R, gamma_est_or, b_est_or)

Arguments

y	a list of the response with the elements y_ID, y_time_point and y_value.
x	a list of the functional covariate with the elements x_ID, x_time_point and x_value.
z	a list of the scalar covariate with the elements z_ID, z_time_point and z_value.
lambda_s	the tuning parameter in the s direction.
lambda_u	the tuning parameter in the u direction.
bd	the bandwidth of the kernel function.
order_1	order of the B-splines in the s direction.
order_2	order of the B-splines in the u direction.
breaks	knots of the B-splines.
boot_R	boostrap times
gamma_est_or	estimate of the scalar parameters using the original estimating equations.
b_est_or	estimate of the functional parameter using the original estimating equations.

Value

boot_gamma	a vector of statistics of the bootstrap samples when testing the scalar parameters.
boot_b	a vector of statistics of the bootstrap samples when testing the functional parameter.
gamma_judge	TRUE or FALSE indicating the significance of the scalar covariates.
b_judge	TRUE or FALSE indicating the significance of the functional covariate.
gamma_pvalue	p-value of testing the nullity of the scalar parameters.
b_pvalue	p-value of testing the nullity of the functional parameters.

References

see the paper "Generalized functional partial varying-coefficient model".

BIG-S2/GFPLVCM documentation built on May 23, 2019, 5:01 a.m.