Advance of the sufficient dimension reduction
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DOI: 10.1002/wics.1516
Abstract: The sufficient dimension reduction of Li has been seen a steady development in the past 30 years in both methodology and application. The main approaches can be categorized into two groups: The inverse regression methods and… read more here.
Keywords: methodology; advance sufficient; dimension reduction; sufficient dimension ... See more keywords
Principal weighted logistic regression for sufficient dimension reduction in binary classification
Sign Up to like & getrecommendations! Published in 2019 at "Journal of the Korean Statistical Society"
DOI: 10.1016/j.jkss.2018.11.001
Abstract: Abstract Sufficient dimension reduction (SDR) is a popular supervised machine learning technique that reduces the predictor dimension and facilitates subsequent data analysis in practice. In this article, we propose principal weighted logistic regression (PWLR), an… read more here.
Keywords: regression; dimension reduction; binary classification; dimension ... See more keywords
On expectile-assisted inverse regression estimation for sufficient dimension reduction
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DOI: 10.1016/j.jspi.2020.11.004
Abstract: Moment-based sufficient dimension reduction methods such as sliced inverse regression may not work well in the presence of heteroscedasticity. We propose to first estimate the expectiles through kernel expectile regression, and then carry out dimension… read more here.
Keywords: inverse regression; dimension reduction; regression; sufficient dimension ... See more keywords
Ultrahigh-dimensional sufficient dimension reduction for censored data with measurement error in covariates
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Applied Statistics"
DOI: 10.1080/02664763.2020.1856352
Abstract: In this paper, we consider the ultrahigh-dimensional sufficient dimension reduction (SDR) for censored data and measurement error in covariates. We first propose the feature screening procedure bas... read more here.
Keywords: data measurement; sufficient dimension; dimensional sufficient; dimension reduction ... See more keywords
Sufficient dimension reduction via distance covariance with multivariate responses
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Nonparametric Statistics"
DOI: 10.1080/10485252.2018.1562065
Abstract: ABSTRACT In this article, we propose a new method for sufficient dimension reduction when both response and predictor are vectors. The new method, using distance covariance, keeps the model-free advantage, and can fully recover the… read more here.
Keywords: dimension reduction; distance covariance; sufficient dimension;
On estimating regression-based causal effects using sufficient dimension reduction
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DOI: 10.1093/biomet/asw068
Abstract: SUMMARY In many causal inference problems the parameter of interest is the regression causal effect, defined as the conditional mean difference in the potential outcomes given covariates. In this paper we discuss how sufficient dimension… read more here.
Keywords: dimension reduction; sufficient dimension; regression; causal ... See more keywords
Slicing-free Inverse Regression in High-dimensional Sufficient Dimension Reduction
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DOI: 10.5705/ss.202022.0112
Abstract: Sliced inverse regression (SIR, Li 1991) is a pioneering work and the most recognized method in sufficient dimension reduction. While promising progress has been made in theory and methods of high-dimensional SIR, two remaining challenges… read more here.
Keywords: high dimensional; inverse regression; dimension reduction; sufficient dimension ... See more keywords