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sdrt (version 1.0.0)

Estimating the Sufficient Dimension Reduction Subspaces in Time Series

Description

The sdrt() function is designed for estimating subspaces for Sufficient Dimension Reduction (SDR) in time series, with a specific focus on the Time Series Central Mean subspace (TS-CMS). The package employs the Fourier transformation method proposed by Samadi and De Alwis (2023) and the Nadaraya-Watson kernel smoother method proposed by Park et al. (2009) for estimating the TS-CMS. The package provides tools for estimating distances between subspaces and includes functions for selecting model parameters using the Fourier transformation method.

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Version

Install

install.packages('sdrt')

Monthly Downloads

181

Version

1.0.0

License

GPL-2 | GPL-3

Maintainer

Tharindu P. De Alwis

Last Published

March 28th, 2024

Functions in sdrt (1.0.0)

sdrt

Estimate the SDR subspaces for univariate time series data.
lynx

Canadian Lynx Data.
dist

Return the distance between two subspaces spanning by column space of matrices.
sigma_u

The tuning parameter for the estimation of the time series central mean subspace
pd.boots

Select the model parameters using Fourier transformation method.