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An ensemble of inverse moment estimators for sufficient dimension reduction
Computational Statistics & Data Analysis ( IF 1.8 ) Pub Date : 2021-04-08 , DOI: 10.1016/j.csda.2021.107241
Qin Wang , Yuan Xue

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.



中文翻译:

一组反矩估计量,以充分减小尺寸

众所周知,充分降维(SDR)是高维数据的数据可视化和信息检索中的有用工具。许多众所周知的SDR方法在给出响应的情况下研究预测变量的逆条件矩。受集合维数缩减的想法启发,我们提出了一个反矩估计量集合,以探索中心子空间。新方法可以大大提高方向的估计精度,而不仅仅是回归均值函数。提出了钢包估计器来确定中心子空间的结构尺寸。我们进一步提出了两种变量选择程序,以提高简化变量的可解释性。仿真研究和实际数据应用都表明了该新方法的有效性。

更新日期:2021-04-21
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