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Preprint
itdr: An R package of Integral Transformation Methods to Estimate the SDR Subspaces in Regression
arXiv (Cornell University)
04/14/2022
Abstract
Sufficient dimension reduction (SDR) is an effective tool for regression models, offering a viable approach to address and analyze the nonlinear nature of regression problems. This paper introduces the itdr R package, a comprehensive and user-friendly tool that introduces several functions based on
integral transformation methods for estimating SDR subspaces. In particular, the itdr package incorporates two key methods, namely the Fourier method (FM) and the convolution method (CM). These methods allow for estimating the SDR subspaces, namely the central mean subspace (CMS) and the central subspace (CS), in cases where the response is univariate. Furthermore, the itdr package facilitates the recovery of the CMS through the iterative Hessian
transformation (IHT) method for univariate responses. Additionally, it enables the recovery of the CS by employing various Fourier transformation strategies, such as the inverse dimension reduction method, the minimum discrepancy approach using Fourier transformation, and the Fourier transform sparse inverse regression approach, specifically designed for cases with multivariate responses. To demonstrate its capabilities, the itdr package is applied to five different datasets. Furthermore, this package is the pioneering implementation of integral transformation methods for estimating SDR subspaces, thus promising significant advancements in SDR research.
Details
- Title
- itdr: An R package of Integral Transformation Methods to Estimate the SDR Subspaces in Regression
- Publication Details
- arXiv (Cornell University)
- Resource Type
- Preprint
- Format
- link
- Number of pages
- 17
- Identifiers
- 99380581190106600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Mathematics and Statistics
- Language
- English