2020
DOI: 10.48550/arxiv.2006.01635
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direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods

Abstract: The direpack package aims to establish a set of modern statistical dimension reduction techniques into the Python universe as a single, consistent package. The dimension reduction methods included resort into three categories: projection pursuit based dimension reduction, sufficient dimension reduction, and robust M estimators for dimension reduction. As a corollary, regularized regression estimators based on these reduced dimension spaces are provided as well, ranging from classical principal component regres… Show more

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Cited by 2 publications
(2 citation statements)
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“…Recent works in bias detection [49] make use of the distance correlation to uncover attributes that act as proxies for sensitive data. Furthermore, distance covariance and correlation are commonly used in dimensionality reduction strategies, both in multivariate [50,51] and functional [52] frameworks.…”
Section: Impact and Applicationsmentioning
confidence: 99%
“…Recent works in bias detection [49] make use of the distance correlation to uncover attributes that act as proxies for sensitive data. Furthermore, distance covariance and correlation are commonly used in dimensionality reduction strategies, both in multivariate [50,51] and functional [52] frameworks.…”
Section: Impact and Applicationsmentioning
confidence: 99%
“…It is practicable to fix the upper bound of iterations N to 200. The tolerance τ is set 0.001 as in Chen et al (2018) and the starting point B 0 is taken to be the nonsparse solution from (2) obtained in python through the direpack package (Menvouta et al, 2020).To choose the penalty parameter θ, values in [0, 0.5] spaced by 0.01 are considered in algorithm 1 and the solution to ( 14) is selected. When the structural dimension h is unknown, it can be estimated using the bootstrap method of Sheng and Yin (2016) with 200 replications.…”
Section: Sparse Sufficient Dimension Reductionmentioning
confidence: 99%