Results 1 to 10 of about 2,014 (224)

Sparse sliced inverse regression for high dimensional data analysis [PDF]

BMC Bioinformatics, 2022
Background Dimension reduction and variable selection play a critical role in the analysis of contemporary high-dimensional data. The semi-parametric multi-index model often serves as a reasonable model for analysis of such high-dimensional data.
Haileab Hilafu, Sandra E. Safo
doaj   +2 more sources

A sliced inverse regression (SIR) decoding the forelimb movement from neuronal spikes in the rat motor cortex [PDF]

Frontiers in Neuroscience, 2016
Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE ...
Shih-Hung Yang, You-Yin Chen, Sheng-Huang Lin, Sheng-Huang Lin, Lun-De Liao, Lun-De Liao, Horng-Shing Lu, Ching-Fu Wang, Po-Chuan Chen, Yu-Chun Lo, Thanh Dat Phan, Hsiang-Ya Chao, Hui-Ching Lin, Hsin-Yi Lai, Wei-Chen Huang   +14 more
doaj   +2 more sources

Multiple phenotype association tests based on sliced inverse regression [PDF]

BMC Bioinformatics
Background Joint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in ...
Wenyuan Sun, Kyongson Jon, Wensheng Zhu, the Alzheimer’s Disease Neuroimaging Initiative   +3 more
doaj   +2 more sources

Kernel Sliced Inverse Regression: Regularization and Consistency [PDF]

Abstract and Applied Analysis, 2013
Kernel sliced inverse regression (KSIR) is a natural framework for nonlinear dimension reduction using the mapping induced by kernels. However, there are numeric, algorithmic, and conceptual subtleties in making the method robust and consistent. We apply
Qiang Wu, Feng Liang, Sayan Mukherjee
doaj   +3 more sources

Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces [PDF]

APL Bioengineering
Neural signal degradation poses a significant challenge in maintaining stable performance when decoding motor tasks using multiunit activity (MUA) and local field potential (LFP) signals in the implantable brain machine interface (iBMI) applications ...
Yun-Ting Kuo, Han-Lin Wang, Bo-Wei Chen, Ching-Fu Wang, Yu-Chun Lo, Sheng-Huang Lin, Po-Chuan Chen, You-Yin Chen   +7 more
doaj   +2 more sources

Sparse Sliced Inverse Regression Via Lasso. [PDF]

J Am Stat Assoc, 2019
For multiple index models, it has recently been shown that the sliced inverse regression (SIR) is consistent for estimating the sufficient dimension reduction (SDR) space if and only if ρ=limpn=0, where p is the dimension and n is the sample size.
Lin Q, Zhao Z, Liu JS.
europepmc   +7 more sources

Adaptive slope reliability analysis method based on sliced inverse regression dimensionality reduction

Frontiers in Ecology and Evolution, 2023
The response surface model has been widely used in slope reliability analysis owing to its efficiency. However, this method still has certain limitations, especially the curse of high dimensionality when considering the spatial variability of ...
Zheng Zhou, Zheng Zhou, Zheng Zhou, Hai-Bin Xiong, Hai-Bin Xiong, Hai-Bin Xiong, Wen-Xia Wu, Wen-Xia Wu, Yi-Jian Yang, Yi-Jian Yang, Yi-Jian Yang, Xu-Hai Yang, Xu-Hai Yang   +12 more
doaj   +1 more source

Student Sliced Inverse Regression [PDF]

Computational Statistics & Data Analysis, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Forbes, Florence, Chiancone, Alessandro, Girard, Stéphane   +2 more
openaire   +6 more sources

Advanced topics in Sliced Inverse Regression

Journal of Multivariate Analysis, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Girard, Stéphane, Lorenzo, Hadrien, Saracco, Jérôme   +2 more
openaire   +4 more sources

Collaborative sliced inverse regression [PDF]

Communications in Statistics - Theory and Methods, 2016
Sliced Inverse Regression (SIR) is an effective method for dimensionality reduction in high-dimensional regression problems. However, the method has requirements on the distribution of the predictors that are hard to check since they depend on unobserved variables.
Chiancone, Alessandro, Girard, Stéphane, Chanussot, Jocelyn   +2 more
openaire   +5 more sources