A review on the generalization of sufficient dimension reduction methods with the additional information

2017 ◽  
Vol 9 (4) ◽  
pp. e1401
Author(s):  
Hung Hung ◽  
Henry Horng-Shing Lu
Keyword(s):  
Dimension Reduction ◽  
Sufficient Dimension Reduction ◽  
Additional Information ◽  
Reduction Methods

scholarly journals Sufficient dimension reduction for compositional data

Biostatistics ◽  
2019 ◽  
Author(s):  
Diego Tomassi ◽  
Liliana Forzani ◽  
Sabrina Duarte ◽  
Ruth M Pfeiffer
Keyword(s):  
Dimension Reduction ◽  
Graphical Models ◽  
Compositional Data ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Outcome Variable ◽  
Sufficient Dimension Reduction ◽  
Inverse Regression ◽  
Linear Combinations ◽  
Reduction Methods

Summary Recent efforts to characterize the human microbiome and its relation to chronic diseases have led to a surge in statistical development for compositional data. We develop likelihood-based sufficient dimension reduction methods (SDR) to find linear combinations that contain all the information in the compositional data on an outcome variable, i.e., are sufficient for modeling and prediction of the outcome. We consider several models for the inverse regression of the compositional vector or transformations of it, as a function of outcome. They include normal, multinomial, and Poisson graphical models that allow for complex dependencies among observed counts. These methods yield efficient estimators of the reduction and can be applied to continuous or categorical outcomes. We incorporate variable selection into the estimation via penalties and address important invariance issues arising from the compositional nature of the data. We illustrate and compare our methods and some established methods for analyzing microbiome data in simulations and using data from the Human Microbiome Project. Displaying the data in the coordinate system of the SDR linear combinations allows visual inspection and facilitates comparisons across studies.

scholarly journals Sufficient dimension reduction with additional information

Biostatistics ◽  
2015 ◽  
Vol 17 (3) ◽  
pp. 405-421 ◽  
Author(s):  
Hung Hung ◽  
Chih-Yen Liu ◽  
Henry Horng-Shing Lu
Keyword(s):  
Dimension Reduction ◽  
Sufficient Dimension Reduction ◽  
Additional Information

scholarly journals Sufficient Dimension Reduction: Methods and Applications With R

2020 ◽  
Vol 115 (530) ◽  
pp. 1032-1033
Author(s):  
Daniel J. McDonald
Keyword(s):  
Dimension Reduction ◽  
Sufficient Dimension Reduction ◽  
Reduction Methods

scholarly journals Cumulative Median Estimation for Sufficient Dimension Reduction

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 138-145
Author(s):  
Stephen Babos ◽  
Andreas Artemiou
Keyword(s):  
Dimension Reduction ◽  
Real Data ◽  
Sufficient Dimension Reduction

In this paper, we present the Cumulative Median Estimation (CUMed) algorithm for robust sufficient dimension reduction. Compared with non-robust competitors, this algorithm performs better when there are outliers present in the data and comparably when outliers are not present. This is demonstrated in simulated and real data experiments.

Dimension reduction methods in graph drawing problem

2014 ◽  
Author(s):  
Lukas Zaoralek ◽  
Tomas Burianek ◽  
Vaclav Snasel
Keyword(s):  
Dimension Reduction ◽  
Graph Drawing ◽  
Reduction Methods

Statistical analysis in EMC using dimension reduction methods

2014 ◽  
Author(s):  
David W P Thomas ◽  
Oluwabukola A Oke ◽  
Christopher Smartt
Keyword(s):  
Statistical Analysis ◽  
Dimension Reduction ◽  
Reduction Methods

Sufficient dimension reduction through informative predictor subspace

Statistics ◽  
2016 ◽  
Vol 50 (5) ◽  
pp. 1086-1099 ◽  
Author(s):  
Jae Keun Yoo
Keyword(s):  
Dimension Reduction ◽  
Sufficient Dimension Reduction

On Partial Sufficient Dimension Reduction With Applications to Partially Linear Multi-Index Models

2013 ◽  
Vol 108 (501) ◽  
pp. 237-246 ◽  
Author(s):  
Zhenghui Feng ◽  
Xuerong Meggie Wen ◽  
Zhou Yu ◽  
Lixing Zhu
Keyword(s):  
Dimension Reduction ◽  
Sufficient Dimension Reduction ◽  
Multi Index ◽  
Partially Linear
Sign in / Sign up

Export Citation Format

Share Document