On the Probability of Correct Selection for LargekPopulations, with Application to Microarray Data

2008 ◽  
Vol 50 (5) ◽  
pp. 870-883 ◽  
Author(s):  
Xinping Cui ◽  
Jason Wilson
Keyword(s):  
Microarray Data ◽  
Correct Selection ◽  
Probability Of Correct Selection ◽  
Selection For

scholarly journals A Procedure For Selecting A Best Multinomial Distribution With Application To Population Income Mobility

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Saad T. Bakir
Keyword(s):  
Linear Combination ◽  
Multinomial Distribution ◽  
Correct Selection ◽  
Income Mobility ◽  
Probability Of Correct Selection ◽  
The One ◽  
Economics Of Happiness

A procedure is developed for selecting a subset which is asserted to contain the “best” of several multinomial populations with a pre-assigned probability of correct selection. According to a pre-chosen linear combination of the multinomial cell probabilities, the “best” population is defined to be the one with the highest such linear combination. As an illustration, the proposed procedure is applied to data relating to the economics of happiness and population income mobility.

Selecting the Best Alternative Based on Its Quantile

2020 ◽  
Author(s):  
Demet Batur ◽  
F. Fred Choobineh
Keyword(s):  
Value At Risk ◽  
Decision Makers ◽  
Correct Selection ◽  
Zone Size ◽  
Indifference Zone ◽  
Probability Of Correct Selection ◽  
A Value ◽  
Conscious Decision ◽  
Target Probability ◽  
Sequential Procedures

A value-at-risk, or quantile, is widely used as an appropriate investment selection measure for risk-conscious decision makers. We present two quantile-based sequential procedures—with and without consideration of equivalency between alternatives—for selecting the best alternative from a set of simulated alternatives. These procedures asymptotically guarantee a user-defined target probability of correct selection within a prespecified indifference zone. Experimental results demonstrate the trade-off between the indifference-zone size and the number of simulation iterations needed to render a correct selection while satisfying a desired probability of correct selection.

scholarly journals Multiobjective feature selection for microarray data via distributed parallel algorithms

2019 ◽  
Vol 100 ◽  
pp. 952-981 ◽  
Author(s):  
Bin Cao ◽  
Jianwei Zhao ◽  
Po Yang ◽  
Peng Yang ◽  
Xin Liu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Parallel Algorithms ◽  
Microarray Data ◽  
Selection For
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