Results 201 to 210 of about 2,014 (224)

Influence Functions for Sliced Inverse Regression

Scandinavian Journal of Statistics, 2005
The sliced inverse regression is a technique of estimation of the best linear index coefficients \(\beta_i\) in a nonparametric regression model \(y=f(\beta_1x,\dots,\beta_k x,\varepsilon)\), where \(y\) is the response, \(x\) is the vector of regressors, \(f\) is an unknown regression function, and \(\varepsilon\) is an error term.
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A note on sliced inverse regression with missing predictors

Statistical Analysis and Data Mining: The ASA Data Science Journal, 2011
AbstractSufficient dimension reduction (SDR) is effective in high‐dimensional data analysis as it mitigates the curse of dimensionality while retaining full regression information. Missing predictors are common in high‐dimensional data, yet are only discussed occasionally in the SDR context. In this paper, an inverse probability weighted sliced inverse
Yuexiao Dong, Liping Zhu
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A Quasi-residuals method in sliced inverse regression

Statistics & Probability Letters, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tian, Maozai, Li, Guoying
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Adaptive slicing for functional slice inverse regression

Statistical Papers
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zheng, Linjuan, Liang, Beiting, Wang, Guochang   +2 more
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On sliced inverse regression with missing values

Journal of Nonparametric Statistics, 2018
To deal with predictors missing at random in sufficient dimension reduction, IPW-SIR (Li and Lu (2008), ‘Sufficient dimension reduction with missing predictors’, Journal of American Statistical Ass...
Yuexiao Dong, Zeda Li
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Regularized Sliced Inverse Regression with Applications in Classification

2007
Consider the problem of classifying a number of objects into one of several groups or classes based on a set of characteristics. This problem has been extensively studied under the general subject of discriminant analysis in the statistical literature, or supervised pattern recognition in the machine learning field.
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Sliced Inverse Regression for Dimension Reduction

Journal of the American Statistical Association, 1991
Abstract Modern advances in computing power have greatly widened scientists' scope in gathering and investigating information from many variables, information which might have been ignored in the past. Yet to effectively scan a large pool of variables is not an easy task, although our ability to interact with data has been much enhanced by recent ...
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High-Dimensional Elliptical Sliced Inverse Regression in Non-Gaussian Distributions

Journal of Business and Economic Statistics, 2022
Xin Chen
exaly  

On the regularization of Sliced Inverse Regression

2010
Sliced Inverse Regression (SIR) is an effective method for dimension reduction in high dimensional regression problems. The original method, however, requires the inversion of the predictors covariance matrix. In case of collinearity between these predictors or small sample sizes compared to the dimension, the inversion is not possible and a ...
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Online kernel sliced inverse regression

Computational Statistics & Data Analysis
Jianjun Xu, Yue Zhao, Haoyang Cheng
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