Variable Reduction and Variable Selection Methods Using Small, Medium and Large Datasets: A Forecast Comparison for the PEEIs

2014 ◽  
Author(s):  
George Kapetanios ◽  
Massimiliano Giuseppe Marcellino ◽  
Fotis Papailias
Keyword(s):  
Variable Selection ◽  
Large Datasets ◽  
Selection Methods ◽  
Variable Reduction ◽  
Forecast Comparison

Variable Selection Methods in QSAR: An Overview

2008 ◽  
Vol 8 (18) ◽  
pp. 1606-1627 ◽  
Author(s):  
Maykel Gonzalez ◽  
Carmen Teran ◽  
Liane Saiz-Urra ◽  
Marta Teijeira
Keyword(s):  
Variable Selection ◽  
Selection Methods

scholarly journals Variable selection methods for water demand forecasting in Ethiopia: Case study Gondar town

2018 ◽  
Vol 4 (1) ◽  
pp. 1537067 ◽  
Author(s):  
Mohammed Gedefaw ◽  
Wang Hao ◽  
Yan Denghua ◽  
Abel Girma ◽  
Mustafa Ibrahim Khamis
Keyword(s):  
Variable Selection ◽  
Water Demand ◽  
Demand Forecasting ◽  
Selection Methods ◽  
Water Demand Forecasting ◽  
Gondar Town

Sparse Estimation and Uncertainty with Application to Subgroup Analysis

2017 ◽  
Vol 25 (1) ◽  
pp. 1-40 ◽  
Author(s):  
Marc Ratkovic ◽  
Dustin Tingley
Keyword(s):  
Variable Selection ◽  
Confidence Intervals ◽  
Public Support ◽  
Theoretical Work ◽  
Bayesian Variable Selection ◽  
Order Effects ◽  
Selection Methods ◽  
Sparse Estimation ◽  
Recent Theoretical Work ◽  
Subgroup Effects

We introduce a Bayesian method, LASSOplus, that unifies recent contributions in the sparse modeling literatures, while substantially extending pre-existing estimators in terms of both performance and flexibility. Unlike existing Bayesian variable selection methods, LASSOplus both selects and estimates effects while returning estimated confidence intervals for discovered effects. Furthermore, we show how LASSOplus easily extends to modeling repeated observations and permits a simple Bonferroni correction to control coverage on confidence intervals among discovered effects. We situate LASSOplus in the literature on how to estimate subgroup effects, a topic that often leads to a proliferation of estimation parameters. We also offer a simple preprocessing step that draws on recent theoretical work to estimate higher-order effects that can be interpreted independently of their lower-order terms. A simulation study illustrates the method’s performance relative to several existing variable selection methods. In addition, we apply LASSOplus to an existing study on public support for climate treaties to illustrate the method’s ability to discover substantive and relevant effects. Software implementing the method is publicly available in theRpackagesparsereg.

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