Large-sample inference for nonparametric regression with dependent errors
Large-sample inference for nonparametric regression with dependent errors
A central limit theorem is given for certain weighted partial sums of a covariance stationary process, assuming it is linear in martingale differences, but without any restriction on its spectrum. We apply the result to kernel nonparametric fixed-design regression, giving a single central limit theorem which indicates how error spectral …