An investigation on the bias reduction in linear variety of ratio-cum-product estimator

2002 ◽  
Vol 86 (3) ◽  
pp. 323-332
Author(s):  
Housila P. Singh ◽  
Sarjinder Singh ◽  
Derrick S. Tracy
Keyword(s):  
Bias Reduction ◽  
Linear Variety ◽  
Product Estimator

The Hydrolysis of Sodium Polyphosphates

1971 ◽  
Vol 26 (6) ◽  
pp. 543-545
Author(s):  
Leopoldo J. Anghileri ◽  
Esther S. Miller
Keyword(s):  
Aqueous Solution ◽  
Slow Reaction ◽  
Linear Variety ◽  
The Cross ◽  
Hydrolysis Of

The hydrolysis of 32P-sodium polyphosphates (linear and cross-linked) in aqueous solution has been studied. The radiometric determinations indicate that the ortho-phosphate formation is a slow reaction, and that the amount formed by the linear variety is higher than that produced by the cross-linked form. There is a significant formation of metaphosphates during the hydrolysis of the cross-linked polyphosphate which is missing or at least reduced to a much lesser extent in the case of the linear polyphosphate.

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
Sign in / Sign up

Export Citation Format

Share Document