sdrt {sdrt}

Estimate the SDR subspaces for univariate time series data.

Description

‘sdrt()’ is the main function to estimate the SDR subspaces in time series.

Usage

sdrt(y, p, d, w1 = 0.1, space = "mean", std = FALSE,
                                 density = "normal", method = "FM",n.grid=10)

Arguments

y

A univariate time series observations.

p

Integer value. The lag of the time series.

d

Integer value (<p). The dimension of the time series central mean subspace.

w1

(default 0.1). The tuning parameter of the “FM” estimation method.

space

(default “mean”). Specify the SDR subspace needed to be estimated.

std

(default FALSE). If TRUE, then standardize the data.

density

(default “kernel”). Specify the density function for the estimation (“kernel” or “normal”).

method

(default “FM”). Specify the estimation method (“FM” or “NW”).

n.grid

(default 10). Number of searches for the initial value in “NW” method

Value

The output is a p-by-d basis matrix for the TS-CMS.

References

Park J. H., Sriram T. N. and Yin X. (2010). Dimension Reduction in Time Series. Statistica Sinica. 20, 747-770.

Samadi S. Y. and De Alwis T. P. (2023). Fourier Method of Estimating Time Series Central Mean Subspace. https://arxiv.org/pdf/2312.02110.

See Also

pd.boots, sigma_u

Examples

data("lynx")
y <- log10(lynx)
p <- 3
d <- 1
fit.model <- sdrt(y, p, d=1,method="FM",density = "kernel")
fit.model$eta_hat