Results 11 to 20 of about 2,991,648 (129)
Robust sliced inverse regression procedures [PDF]
, 1998 Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Several properties of this relatively new method have been examined already, but little attention has been paid to robustness aspects.
Gather, Ursula +2 more
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Advanced topics in Sliced Inverse Regression [PDF]
, 2021 Since its introduction in the early 90's, the Sliced Inverse Regression (SIR) methodology has evolved adapting to increasingly complex data sets in contexts combining linear dimension reduction with non linear regression.
SARACCO, Jérôme +2 more
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Parameter reduction for variability analysis by slice inverse regression method
IET Circuits, Devices & Systems, 2007 With semiconductor fabrication technologies scaled below 100 nm, the design-manufacturing interface becomes more and more complicated. The resultant process variability causes a number of issues in the new generation IC design. One of the biggest challenges is the enormous number of process variation related parameters.
Alexander V. Mitev +3 more
openaire +1 more source
Isometric Sliced Inverse Regression for Nonlinear Manifolds Learning [PDF]
, 2012 Sliced inverse regression (SIR) was developed to find effective linear dimension-reduction directions for exploring the intrinsic structure of the high-dimensional data.
Yao, Wei-ting; Wu, Han-ming
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Online Kernel Sliced Inverse Regression [PDF]
, 2023 Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel principal component analysis and other methods have been studied in depth ...
Cui, Wenquan +3 more
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An investigation of sliced inverse regression with censored data. [PDF]
, 2010 The complexity of high-dimensional data creates a number of concerns when trying to analyze it. This data often consists of a response or survival time and potentially thousands of predictors.
Riggs, Daniel W.
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Iterative Projection Approach of Fused Sliced Inverse Regression
, 2021 As the dimensions increase, the space will increase exponentially, which will reduce the density of information and reduce explanatory power. This is described as a curse of dimensionality, and dimension reduction is used to solve this problem ...
한효선
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Fused sliced inverse regression in survival analysis
, 2018 Sufficient dimension reduction (SDR) replaces original p-dimensional predictors to a lower-dimensional linearly transformed predictor. The sliced inverse regression (SIR) has the longest and most popular history of SDR methodologies.
유재근
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Stationary Subspace Analysis for Spatial Data
Environmetrics, Volume 37, Issue 6, September 2026.ABSTRACT Stationary subspace analysis (SSA) is a blind source separation framework that decomposes linearly mixed multivariate data into stationary and nonstationary components. We extend SSA to spatially indexed data by introducing spatial stationary subspace analysis (spSSA), which explicitly accounts for spatial dependence.
Perttu Saarela +3 more
wiley +1 more source
On Sliced Inverse Regression [PDF]
, 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.
LISKI, EERO
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