| sigma_u {sdrt} | R Documentation |
The tuning parameter for the estimation of the time series central mean subspace
Description
‘sigma_u()’ estimates the turning parameter needed to estimate time series central mean subspace in Fourier Method.
Usage
sigma_u(y, p, d, w1_list=seq(0.1,0.5,by=0.1),space="mean",
std=FALSE,density="kernel",method="FM",B=20)
Arguments
y |
A univariate time series observations. |
p |
Integer value. The lag of the time series. |
d |
Integer value. The dimension of the time series central mean subspace. |
w1_list |
(default {0.1, 0.2,0.3,0.4,0.5}). The sequence of candidate list for the tuning parameter. |
space |
(default “mean”). Specify the SDR subspace needed to be estimated. |
std |
(default FALSE). If TRUE, then standardizing the time series observations. |
density |
(default “kernel”). Specify the density function for the estimation (“kernel” or “normal”). |
method |
(default “FM”). Specify the estimation method. (“FM” or “NW”). |
B |
(default 20). Number of block bootstrap samples. |
Value
The output is a length(sw2_seq) dimensional vector.
dis_sw2 |
The average block boostrap distances for each candidate list of values. |
References
Samadi S. Y. and De Alwis T. P. (2023). Fourier Method of Estimating Time Series Central Mean Subspace. https://arxiv.org/pdf/2312.02110.
Examples
data("lynx")
y <- log10(lynx)
p <- 3
d <- 1
w1_list=seq(0.1,0.5,by=0.1)
Tuning.model=sigma_u(y, p, d, w1_list=w1_list, std=FALSE, B=10)
Tuning.model$sigma_u_hat