scholarly journals A simple new approach to variable selection in regression, with application to genetic fine mapping

2020 ◽  
Vol 82 (5) ◽  
pp. 1273-1300 ◽  
Author(s):  
Gao Wang ◽  
Abhishek Sarkar ◽  
Peter Carbonetto ◽  
Matthew Stephens
Keyword(s):  
Variable Selection ◽  
Fine Mapping ◽  
New Approach

scholarly journals A simple new approach to variable selection in regression, with application to genetic fine-mapping

2018 ◽  
Author(s):  
Gao Wang ◽  
Abhishek Sarkar ◽  
Peter Carbonetto ◽  
Matthew Stephens
Keyword(s):  
Variable Selection ◽  
Fine Mapping ◽  
Posterior Distribution ◽  
Zero Element ◽  
Variational Approximation ◽  
New Approach ◽  
Stepwise Selection ◽  
Fitting Procedure ◽  
Highly Correlated ◽  
Credible Set

We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model — the “Sum of Single Effects” (SuSiE) model — which comes from writing the sparse vector of regression coefficients as a sum of “single-effect” vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure — Iterative Bayesian Stepwise Selection (IBSS) — which is a Bayesian analogue of stepwise selection methods. IBSS shares the computational simplicity and speed of traditional stepwise methods, but instead of selecting a single variable at each step, IBSS computes a distribution on variables that captures uncertainty in which variable to select. We provide a formal justification of this intuitive algorithm by showing that it optimizes a variational approximation to the posterior distribution under the SuSiE model. Further, this approximate posterior distribution naturally yields convenient novel summaries of uncertainty in variable selection, providing a Credible Set of variables for each selection. Our methods are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse, both of which are characteristics of genetic fine-mapping applications. We demonstrate through numerical experiments that our methods outper-form existing methods for this task, and illustrate their application to fine-mapping genetic variants influencing alternative splicing in human cell-lines. We also discuss the potential and challenges for applying these methods to generic variable selection problems.

A New Approach to Variable Selection Using the TLS Approach

2007 ◽  
Vol 55 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Jean-Jacques Fuchs ◽  
Sbastien Maria
Keyword(s):  
Variable Selection ◽  
New Approach

scholarly journals Bayesian Lasso Tobit regression

2019 ◽  
Vol 11 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Haider Kadhim Abbas
Keyword(s):  
Model Selection ◽  
Variable Selection ◽  
Covariance Matrix ◽  
Gibbs Sampler ◽  
Real Data ◽  
New Technique ◽  
Tobit Regression ◽  
Bayesian Lasso ◽  
New Approach ◽  
Selection Property

In the present research, we have proposed a new approach for model selection in Tobit regression. The new technique uses Bayesian Lasso in Tobit regression (BLTR). It has many features that give optimum estimation and variable selection property. Specifically, we introduced a new hierarchal model. Then, a new Gibbs sampler is introduced.We also extend the new approach by adding the ridge parameter inside the variance covariance matrix to avoid the singularity in the case of multicollinearity or in case the number of predictors greater than the number of observations. A comparison was made with other previous techniques applying the simulation examples and real data. It is worth mentioning, that the obtained results were promising and encouraging, giving better results compared to the previous methods.

scholarly journals Binary quantile regression and variable selection: A new approach

2018 ◽  
Vol 38 (6) ◽  
pp. 679-694 ◽  
Author(s):  
Katerina Aristodemou ◽  
Jian He ◽  
Keming Yu
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
New Approach ◽  
Binary Quantile Regression
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