Results 121 to 130 of about 11,532 (157)

Localized Sliced Inverse Regression

Journal of Computational and Graphical Statistics, 2010
We develop a supervised dimension reduction method that integrates the idea of localization from manifold learning with the sliced inverse regression framework. We call our method localized sliced inverse regression (LSIR) since it takes into account the local structure of the explanatory variables.
Qiang Wu, Feng Liang, Sayan Mukherjee
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Bagging Versions of Sliced Inverse Regression

Communications in Statistics - Theory and Methods, 2010
Sliced Inverse Regression (SIR) introduced by Li (1991) is a well-known dimension reduction method in semiparametric regression. In this article, we propose bagging versions of SIR which consist in using bootstrap replications of the data set and in aggregating the corresponding estimators.
Saracco, Jérôme, Liquet, Benoit, Kuentz, Vanessa   +2 more
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Sliced mean variance–covariance inverse regression

Computational Statistics & Data Analysis, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sheather, Simon J., McKean, Joseph W., Crimin, Kimberly   +2 more
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Random sliced inverse regression

Communications in Statistics - Simulation and Computation, 2015
ABSTRACTSliced Inverse Regression (SIR; 1991) is a dimension reduction method for reducing the dimension of the predictors without losing regression information. The implementation of SIR requires inverting the covariance matrix of the predictors—which has hindered its use to analyze high-dimensional data where the number of predictors exceed the ...
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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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Robust functional sliced inverse regression

Statistical Papers, 2015
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Wang, Guochang, Zhou, Jianjun, Wu, Wuqing, Chen, Min   +3 more
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Functional sliced inverse regression analysis

Statistics, 2003
Most of the usual multivariate methods have been extended to the context of functional data analysis. Our contribution concerns the study of sliced inverse regression (SIR) when the response variable is real but the regressor is a function. In the first part, we show how the relevant properties of SIR remain essentially the same in the functional ...
L. Ferré, A. F. Yao
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Adaptive slicing for functional slice inverse regression

Statistical Papers
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Zheng, Linjuan, Liang, Beiting, Wang, Guochang   +2 more
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Sliced inverse regression for multivariate response regression

Journal of Statistical Planning and Inference, 2009
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Sliced inverse regression under linear constraints

Communications in Statistics - Theory and Methods, 1999
We consider the semiparametric regression model introduced by Li (1991) and add to this model some linear constraints on the slope parameters. These constraints can be identifiability conditions or they may carry additional in¬formations on the slope parameters. Using a geometric argument, we develop a method to estimate the slope parameters. This link-
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