scholarly journals Penalized regression calibration: A method for the prediction of survival outcomes using complex longitudinal and high‐dimensional data

2021 ◽  
Author(s):  
Mirko Signorelli ◽  
Pietro Spitali ◽  
Cristina Al‐Khalili Szigyarto ◽  
Roula Tsonaka ◽  
Keyword(s):  
High Dimensional Data ◽  
Penalized Regression ◽  
High Dimensional ◽  
Survival Outcomes ◽  
Regression Calibration ◽  
Prediction Of Survival

scholarly journals Sample Size Considerations of Prediction-Validation Methods in High-Dimensional Data for Survival Outcomes

2013 ◽  
Vol 37 (3) ◽  
pp. 276-282 ◽  
Author(s):  
Herbert Pang ◽  
Sin-Ho Jung
Keyword(s):  
Sample Size ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Survival Outcomes ◽  
Validation Methods

An Alternating Direction Method of Multipliers for MCP-penalized Regression with High-dimensional Data

2018 ◽  
Vol 34 (12) ◽  
pp. 1892-1906 ◽  
Author(s):  
Yue Yong Shi ◽  
Yu Ling Jiao ◽  
Yong Xiu Cao ◽  
Yan Yan Liu
Keyword(s):  
High Dimensional Data ◽  
Penalized Regression ◽  
Alternating Direction Method ◽  
High Dimensional ◽  
Method Of Multipliers ◽  
Alternating Direction

scholarly journals Proposing Robust LAD-Atan Penalty of Regression Model Estimation for High Dimensional Data

2020 ◽  
Vol 17 (2) ◽  
pp. 0550
Author(s):  
Ali Hameed Yousef ◽  
Omar Abdulmohsin Ali
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
High Dimensional Data ◽  
Real Data ◽  
Penalized Regression ◽  
Good Method ◽  
Superior Performance ◽  
High Dimensional ◽  
Absolute Deviation ◽  
Heavy Tailed

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the proposed LAD-Atan estimator has superior performance compared with other estimators.  

scholarly journals A Comparison of Multifactor Dimensionality Reduction and L1-Penalized Regression to Identify Gene-Gene Interactions in Genetic Association Studies

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Stacey Winham ◽  
Chong Wang ◽  
Alison A Motsinger-Reif
Keyword(s):  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Human Genetics ◽  
Association Studies ◽  
High Dimensional Data ◽  
Penalized Regression ◽  
High Dimensional ◽  
Gene Interactions ◽  
Advantages And Disadvantages ◽  
Main Effects

Recently, the amount of high-dimensional data has exploded, creating new analytical challenges for human genetics. Furthermore, much evidence suggests that common complex diseases may be due to complex etiologies such as gene-gene interactions, which are difficult to identify in high-dimensional data using traditional statistical approaches. Data-mining approaches are gaining popularity for variable selection in association studies, and one of the most commonly used methods to evaluate potential gene-gene interactions is Multifactor Dimensionality Reduction (MDR). Additionally, a number of penalized regression techniques, such as Lasso, are gaining popularity within the statistical community and are now being applied to association studies, including extensions for interactions. In this study, we compare the performance of MDR, the traditional lasso with L1 penalty (TL1), and the group lasso for categorical data with group-wise L1 penalty (GL1) to detect gene-gene interactions through a broad range of simulations.We find that each method has both advantages and disadvantages, and relative performance is context dependent. TL1 frequently over-fits, identifying false positive as well as true positive loci. MDR has higher power for epistatic models that exhibit independent main effects; for both Lasso methods, main effects tend to dominate. For purely epistatic models, GL1 has the best performance for lower minor allele frequencies, but MDR performs best for higher frequencies. These results provide guidance of when each approach might be best suited for detecting and characterizing interactions with different mechanisms.

scholarly journals Analysis of Penalized Regression Methods in a Simple Linear Model on the High-Dimensional Data

2019 ◽  
Vol 8 (5) ◽  
pp. 185
Author(s):  
Zari Farhadi Zari Farhadi ◽  
Reza Arabi Belaghi ◽  
Ozlem Gurunlu Alma
Keyword(s):  
Linear Model ◽  
High Dimensional Data ◽  
Penalized Regression ◽  
High Dimensional ◽  
Simple Linear Model ◽  
Regression Methods

Fractal-Based Methods as a Technique for Estimating the Intrinsic Dimensionality of High-Dimensional Data: A Survey

Informatica ◽  
2016 ◽  
Vol 27 (2) ◽  
pp. 257-281 ◽  
Author(s):  
Rasa Karbauskaitė ◽  
Gintautas Dzemyda
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Intrinsic Dimensionality

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional
Sign in / Sign up

Export Citation Format

Share Document