scholarly journals Variable selection and estimation in high‐dimensional models

2015 ◽  
Vol 48 (2) ◽  
pp. 389-407 ◽  
Author(s):  
Joel L. Horowitz
Keyword(s):  
Variable Selection ◽  
High Dimensional ◽  
Dimensional Models ◽  
Variable Selection And Estimation

scholarly journals Iterative Variable Selection for High-Dimensional Data: Prediction of Pathological Response in Triple-Negative Breast Cancer

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 222
Author(s):  
Juan C. Laria ◽  
M. Carmen Aguilera-Morillo ◽  
Enrique Álvarez ◽  
Rosa E. Lillo ◽  
Sara López-Taruella ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Variable Selection ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
A Priori ◽  
Simulated Data ◽  
Point Of View ◽  
High Dimensional ◽  
Whole Genome ◽  
Genome Context

Over the last decade, regularized regression methods have offered alternatives for performing multi-marker analysis and feature selection in a whole genome context. The process of defining a list of genes that will characterize an expression profile remains unclear. It currently relies upon advanced statistics and can use an agnostic point of view or include some a priori knowledge, but overfitting remains a problem. This paper introduces a methodology to deal with the variable selection and model estimation problems in the high-dimensional set-up, which can be particularly useful in the whole genome context. Results are validated using simulated data and a real dataset from a triple-negative breast cancer study.

Inference for treatment effect parameters in potentially misspecified high-dimensional models

Biometrika ◽  
2020 ◽  
Author(s):  
Oliver Dukes ◽  
Stijn Vansteelandt
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Observational Studies ◽  
Treatment Effects ◽  
Treatment Selection ◽  
High Dimensional ◽  
Selection Mechanism ◽  
Dimensional Models ◽  
Effect Parameter

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.

scholarly journals The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA?

2014 ◽  
Vol 29 (4) ◽  
pp. 619-639 ◽  
Author(s):  
Y. Ritov ◽  
P. J. Bickel ◽  
A. C. Gamst ◽  
B. J. K. Kleijn
Keyword(s):  
Bayesian Analysis ◽  
High Dimensional ◽  
Dimensional Models
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