A split-and-merge Bayesian variable selection approach for ultrahigh dimensional regression

2014 ◽  
Vol 77 (5) ◽  
pp. 947-972 ◽  
Author(s):  
Qifan Song ◽  
Faming Liang
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Selection Approach ◽  
Split And Merge

Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

2019 ◽  
Author(s):  
Sierra Bainter ◽  
Thomas Granville McCauley ◽  
Tor D Wager ◽  
Elizabeth Reynolds Losin
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Limited Information ◽  
Online Application ◽  
Stochastic Search Variable Selection ◽  
Selection Approach ◽  
Pain Ratings ◽  
Research Questions ◽  
Standard Techniques ◽  
Search Variable

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.

scholarly journals Identifying Activity-sensitive Spectral Lines: A Bayesian Variable Selection Approach

2019 ◽  
Vol 158 (5) ◽  
pp. 210
Author(s):  
Bo Ning ◽  
Alexander Wise ◽  
Jessi Cisewski-Kehe ◽  
Sarah Dodson-Robinson ◽  
Debra Fischer
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Spectral Lines ◽  
Selection Approach

scholarly journals Gene selection: a Bayesian variable selection approach

2003 ◽  
Vol 19 (1) ◽  
pp. 90-97 ◽  
Author(s):  
K. E. Lee ◽  
N. Sha ◽  
E. R. Dougherty ◽  
M. Vannucci ◽  
B. K. Mallick
Keyword(s):  
Variable Selection ◽  
Gene Selection ◽  
Bayesian Variable Selection ◽  
Selection Approach

scholarly journals On the drivers of profitability in the banking industry in restructuring times: a Bayesian perspective

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paula Cruz-García ◽  
Anabel Forte ◽  
Jesús Peiró-Palomino
Keyword(s):  
Variable Selection ◽  
Ad Hoc ◽  
Banking Industry ◽  
Theoretical Models ◽  
Bayesian Variable Selection ◽  
Content Type ◽  
Net Interest Margin ◽  
Selection Approach ◽  
Main Component ◽  
Interest Margin

Purpose There is abundant literature analyzing the determinants of banks’ profitability through its main component: the net interest margin. Some of these determinants are suggested by seminal theoretical models and subsequent expansions. Others are ad-hoc selections. Up to now, there are no studies assessing these models from a Bayesian model uncertainty perspective. This paper aims to analyze this issue for the EU-15 countries for the period 2008-2014, which mainly corresponds to the Great Recession years. Design/methodology/approach It follows a Bayesian variable selection approach to analyze, in a first step, which variables of those suggested by the literature are actually good predictors of banks’ net interest margin. In a second step, using a model selection approach, the authors select the model with the best fit. Finally, the paper provides inference and quantifies the economic impact of the variables selected as good candidates. Findings The results widely support the validity of the determinants proposed by the seminal models, with only minor discrepancies, reinforcing their capacity to explain net interest margin disparities also during the recent period of restructuring of the banking industry. Originality/value The paper is, to the best of the knowledge, the first one following a Bayesian variable selection approach in this field of the literature.

How to Separate the Wheat from the Chaff: Improved Variable Selection for New Customer Acquisition

2017 ◽  
Vol 81 (2) ◽  
pp. 99-113 ◽  
Author(s):  
Sebastian Tillmanns ◽  
Frenkel Ter Hofstede ◽  
Manfred Krafft ◽  
Oliver Goetz
Keyword(s):  
Variable Selection ◽  
Principal Component ◽  
Bayesian Variable Selection ◽  
Customer Acquisition ◽  
Insurance Company ◽  
Functional Relationships ◽  
Large Numbers ◽  
Selection Approach ◽  
Selection For ◽  
Post Hoc

Steady customer losses create pressure for firms to acquire new accounts, a task that is both costly and risky. Lacking knowledge about their prospects, firms often use a large array of predictors obtained from list vendors, which in turn rapidly creates massive high-dimensional data problems. Selecting the appropriate variables and their functional relationships with acquisition probabilities is therefore a substantial challenge. This study proposes a Bayesian variable selection approach to optimally select targets for new customer acquisition. Data from an insurance company reveal that this approach outperforms nonselection methods and selection methods based on expert judgment as well as benchmarks based on principal component analysis and bootstrap aggregation of classification trees. Notably, the optimal results show that the Bayesian approach selects panel-based metrics as predictors, detects several nonlinear relationships, selects very large numbers of addresses, and generates profits. In a series of post hoc analyses, the authors consider prospects’ response behaviors and cross-selling potential and systematically vary the number of predictors and the estimated profit per response. The results reveal that more predictors and higher response rates do not necessarily lead to higher profits.

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