Model selection bias and Freedman’s paradox

2009 ◽  
Vol 62 (1) ◽  
pp. 117-125 ◽  
Author(s):  
Paul M. Lukacs ◽  
Kenneth P. Burnham ◽  
David R. Anderson
Keyword(s):  
Model Selection ◽  
Selection Bias ◽  
Freedman’S Paradox ◽  
Model Selection Bias

scholarly journals Correction for Model Selection Bias Using a Modified Model Averaging Approach for Supervised Learning Methods Applied to EEG Experiments

2010 ◽  
Vol 20 (4) ◽  
pp. 768-786
Author(s):  
Kristien Wouters ◽  
José Cortiñas Abrahantes ◽  
Geert Molenberghs ◽  
Helena Geys ◽  
Abdellah Ahnaou ◽  
...  
Keyword(s):  
Model Selection ◽  
Supervised Learning ◽  
Selection Bias ◽  
Model Averaging ◽  
Learning Methods ◽  
Modified Model ◽  
Model Selection Bias

scholarly journals Avoiding model selection bias in small-sample genomic datasets

2006 ◽  
Vol 22 (10) ◽  
pp. 1245-1250 ◽  
Author(s):  
D. Berrar ◽  
I. Bradbury ◽  
W. Dubitzky
Keyword(s):  
Model Selection ◽  
Selection Bias ◽  
Small Sample ◽  
Model Selection Bias

scholarly journals Null Distribution of the Test Statistic for Model Selection via Marginal Screening: Implications for Multivariate Regression Analysis

2021 ◽  
pp. 23-31
Author(s):  
A. V. Rubanovich ◽  
V. A. Saenko
Keyword(s):  
Regression Analysis ◽  
Model Selection ◽  
False Positive ◽  
Null Distribution ◽  
Multivariate Regression Analysis ◽  
Test Statistics ◽  
Test Statistic ◽  
Spreadsheet Program ◽  
Freedman’S Paradox ◽  
Analytical Expressions

Marginal screening (MS) is the computationally simple and commonly used for the dimension reduction procedures. In it, a linear model is constructed for several top predictors, chosen according to the absolute value of marginal correlations with the dependent variable. Importantly, when kpredictors out of mprimary covariates are selected, the standard regression analysis may yield false-positive results if m>> k(Freedman's paradox). In this work, we provide analytical expressions describing null distribution of the test statistics for model selection via MS. Using the theory of order statistics, we show that under MS, the common F-statistic is distributed as a mean of ktop variables out of mindependent random variables having a 21χdistribution. Based on this finding, we estimated critical p-values for multiple regression models after MS, comparisons with which of those obtained in real studies will help researchers to avoid false-positive result. Analytical solutions obtained in the work are implemented in a free Excel spreadsheet program.

In Reply to ‘No Obvious Impact of NSAIDs on Risk of Kidney Failure: Causal or Another Selection Bias’

2020 ◽  
Vol 76 (5) ◽  
pp. 742-743 ◽  
Author(s):  
Min Zhan ◽  
Rebecca M. Doerfler ◽  
Jeffrey C. Fink
Keyword(s):  
Selection Bias ◽  
Kidney Failure

ON SUITABLE MODEL SELECTION APPROACH FOR ANALYSIS OF MENTAL HEALTH SERVICE USE DATA

2019 ◽  
Vol 43 (2) ◽  
pp. 212-222
Author(s):  
DULAL K. BHAUMIK ◽  
◽  
SUBHASH ARYAL ◽  
SCOTT HUR ◽  
◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Service ◽  
Model Selection ◽  
Health Service ◽  
Service Use ◽  
Mental Health Service Use ◽  
Health Service Use ◽  
Suitable Model ◽  
Model Selection Approach ◽  
Selection Approach
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