Results 31 to 40 of about 11,532 (157)

A new sliced inverse regression method for multivariate response [PDF]

, 2013
International audienceA semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional covariate x is considered. A new approach is proposed based on sliced inverse regression (SIR) for estimating the effective dimension ...
Coudret, Raphaël, Girard, Stéphane, Saracco, Jerome   +2 more
core   +6 more sources

Integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer.

PLoS ONE, 2014
CRC cancer is one of the deadliest diseases in Western countries. In order to develop prognostic biomarkers for CRC (colorectal cancer) aggressiveness, we analyzed retrospectively 267 CRC patients via a novel, multidimensional biomarker platform.
Marisa Mariani, Shiquan He, Mark McHugh, Mirko Andreoli, Deep Pandya, Steven Sieber, Zheyang Wu, Paul Fiedler, Shohreh Shahabi, Cristiano Ferlini   +9 more
doaj   +1 more source

Counting Process Based Dimension Reduction Methods for Censored Outcomes [PDF]

, 2018
We propose a class of dimension reduction methods for right censored survival data using a counting process representation of the failure process. Semiparametric estimating equations are constructed to estimate the dimension reduction subspace for the ...
Sun, Qiang, Wang, Tao, Zeng, Donglin, Zhu, Ruoqing   +3 more
core   +3 more sources

Randomized Dimension Reduction on Massive Data [PDF]

, 2013
Scalability of statistical estimators is of increasing importance in modern applications and dimension reduction is often used to extract relevant information from data.
Georgiev, Stoyan, Mukherjee, Sayan
core  

Penalized single-index quantile regression [PDF]

, 2013
This article is made available through the Brunel Open Access Publishing Fund. Copyright for this article is retained by the author(s), with first publication rights granted to the journal.
Yu, K
core   +1 more source

High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables [PDF]

, 2013
In this work we address the problem of approximating high-dimensional data with a low-dimensional representation. We make the following contributions. We propose an inverse regression method which exchanges the roles of input and response, such that the ...
Deleforge, Antoine, Forbes, Florence, Horaud, Radu   +2 more
core   +7 more sources

Slope reliability analysis considering spatial variability of parameters based on efficient surrogate model

Yantu gongcheng xuebao
When evaluating the slope reliability with small failure probability considering the spatial variability of parameters, the traditional reliability analysis methods are often time-consuming or difficult to solve.
DENG Zhiping 1, ZHONG Min 1, PAN Min 1, 2, ZHENG Kehong 1, NIU Jingtai 1, JIANG Shuihua 2
doaj   +1 more source

Contour projected dimension reduction

, 2009
In regression analysis, we employ contour projection (CP) to develop a new dimension reduction theory. Accordingly, we introduce the notions of the central contour subspace and generalized contour subspace.
Luo, Ronghua, Tsai, Chih-Ling, Wang, Hansheng   +2 more
core   +1 more source

Asymptotics of sliced inverse regression [PDF]

, 1995
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 ...
Ng, KW, Zhu, L
core  

Testing predictor contributions in sufficient dimension reduction

, 2004
We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for the conditional distribution of the response given the predictors.
Cook, R. Dennis
core   +3 more sources