Bias Reduction for Bayesian and Frequentist Estimators

2005 ◽  
Author(s):  
Alan Bester ◽  
Christian Hansen
Keyword(s):  
Bias Reduction

scholarly journals An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings

2021 ◽  
Vol 23 (1) ◽  
pp. 69-85
Author(s):  
Hemank Lamba ◽  
Kit T. Rodolfa ◽  
Rayid Ghani
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Empirical Work ◽  
Bias Reduction ◽  
Research Community ◽  
High Stakes ◽  
Social Good ◽  
Social Service Delivery ◽  
Reduction Methods ◽  
Applications Of Machine Learning

Applications of machine learning (ML) to high-stakes policy settings - such as education, criminal justice, healthcare, and social service delivery - have grown rapidly in recent years, sparking important conversations about how to ensure fair outcomes from these systems. The machine learning research community has responded to this challenge with a wide array of proposed fairness-enhancing strategies for ML models, but despite the large number of methods that have been developed, little empirical work exists evaluating these methods in real-world settings. Here, we seek to fill this research gap by investigating the performance of several methods that operate at different points in the ML pipeline across four real-world public policy and social good problems. Across these problems, we find a wide degree of variability and inconsistency in the ability of many of these methods to improve model fairness, but postprocessing by choosing group-specific score thresholds consistently removes disparities, with important implications for both the ML research community and practitioners deploying machine learning to inform consequential policy decisions.

Nonresponse in the Dutch Time Use Survey: Strategies for Response Enhancement and Bias Reduction

Field Methods ◽  
2008 ◽  
Vol 21 (1) ◽  
pp. 69-90 ◽  
Author(s):  
Erik Van Ingen ◽  
Ineke Stoop ◽  
Koen Breedveld
Keyword(s):  
Time Use ◽  
Bias Reduction ◽  
Time Use Survey ◽  
Response Enhancement

Bias Reduction Using Mahalanobis-Metric Matching

Biometrics ◽  
1980 ◽  
Vol 36 (2) ◽  
pp. 293 ◽  
Author(s):  
Donald B. Rubin
Keyword(s):  
Bias Reduction ◽  
Metric Matching

Use of simulation–extrapolation estimation in catch–effort analyses

1999 ◽  
Vol 56 (7) ◽  
pp. 1234-1240
Author(s):  
W R Gould ◽  
L A Stefanski ◽  
K H Pollock
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Mean Squared Error ◽  
Estimation Procedure ◽  
Bias Reduction ◽  
Estimation Methods ◽  
Effort Estimation ◽  
Stat Assoc ◽  
General Terms ◽  
Naive Estimator

All catch-effort estimation methods implicitly assume catch and effort are known quantities, whereas in many cases, they have been estimated and are subject to error. We evaluate the application of a simulation-based estimation procedure for measurement error models (J.R. Cook and L.A. Stefanski. 1994. J. Am. Stat. Assoc. 89: 1314-1328) in catch-effort studies. The technique involves a simulation component and an extrapolation step, hence the name SIMEX estimation. We describe SIMEX estimation in general terms and illustrate its use with applications to real and simulated catch and effort data. Correcting for measurement error with SIMEX estimation resulted in population size and catchability coefficient estimates that were substantially less than naive estimates, which ignored measurement errors in some cases. In a simulation of the procedure, we compared estimators from SIMEX with "naive" estimators that ignore measurement errors in catch and effort to determine the ability of SIMEX to produce bias-corrected estimates. The SIMEX estimators were less biased than the naive estimators but in some cases were also more variable. Despite the bias reduction, the SIMEX estimator had a larger mean squared error than the naive estimator for one of two artificial populations studied. However, our results suggest the SIMEX estimator may outperform the naive estimator in terms of bias and precision for larger populations.

Sign in / Sign up

Export Citation Format

Share Document