Results 61 to 70 of about 11,532 (157)
Sufficient dimension reduction based on an ensemble of minimum average variance estimators
, 2012 We introduce a class of dimension reduction estimators based on an ensemble of the minimum average variance estimates of functions that characterize the central subspace, such as the characteristic functions, the Box--Cox transformations and wavelet ...
Li, Bing, Yin, Xiangrong
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Misspecification and Heterogeneity in Single-Index, Binary Choice Models [PDF]
We propose a nonparametric approach for estimating single-index, binary-choice models when parametric models such as Probit and Logit are potentially misspecified.
Chen, Pian, Velamuri, Malathi
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An Asymptotic Theory for Sliced Inverse Regression
The Annals of Statistics, 1992 Sliced inverse regression is a nonparametric method for achieving dimension reduction in regression problems. It is assumed that the conditional distribution of response \(Y\) given predictors \(X\) depends only on \(K\) linear combinations of \(X\).
Hsing, Tailen, Carroll, Raymond J.
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Estimation of Mars surface physical properties from hyperspectral images using Sliced Inverse Regression [PDF]
, 2007 Visible and near infrared imaging spectroscopy is a key remote sensing technique to study and monitor planet Mars. Indeed it allows the detection, mapping and characterization of minerals as well as volatile species that often constitute the first step ...
Bernard-Michel, Caroline +3 more
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A note on the choice of the number of slices in sliced inverse regression [PDF]
Sliced inverse regression (SIR) is a clever technique for reducing the dimension of the predictor in regression problems, thus avoiding the curse of dimensionality. There exist many contributions on various aspects of the performance of SIR.
Becker, Claudia, Gather, Ursula
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, 2009 In statistics, dimension reduction is a method to reduce the number of variables, which will then be considered in the future analysis of the data. Often the new variables are just suitably chosen linear combinations of the original variables X1, ...,Xp.
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Analysis of microarray right-censored data through fused sliced inverse regression. [PDF]
Sci Rep, 2019 Yoo JK.
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Asymptotics of sliced inverse regression
, 2007 Sliced Inverse Regression is a method for reducing the dimension of the explanatory variables x in non-parametric regression problems. Li (1991) discussed a version of this method which begins with a partition of the range of y into slices so that the conditional covariance matrix of x given y can be estimated by the sample covariance matrix within ...
Zhu, L, Ng, KW
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