An ensemble of inverse moment estimators for sufficient dimension reduction

Qin Wang and Yuan Xue

Computational Statistics & Data Analysis, 2021, vol. 161, issue C

Abstract: Sufficient dimension reduction (SDR) is known to be a useful tool in data visualization and information retrieval for high dimensional data. Many well-known SDR approaches investigate the inverse conditional moments of the predictors given the response. Motivated by the idea of the aggregate dimension reduction, we propose an ensemble of inverse moment estimators to explore the central subspace. The new approach can substantially improve the estimation accuracy for the directions beyond the regression mean functions. A ladle estimator is proposed to determine the structural dimension of the central subspace. We further present two variable selection procedures to improve the interpretability of the reduced variables. Both simulation studies and a real data application show the efficacy of the newly proposed method.

Keywords: Aggregate dimension reduction; Central subspace; Ensemble estimator; Sliced inverse regression; Sufficient dimension reduction (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:161:y:2021:i:c:s016794732100075x

DOI: 10.1016/j.csda.2021.107241

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