Combining Probability Values from Independent Permutation Tests: A Discrete Analog of Fisher's Classical Method

2004 ◽  
Vol 95 (2) ◽  
pp. 449-458 ◽  
Author(s):  
Paul W. Mielke ◽  
Janis E. Johnston ◽  
Kenneth J. Berry
Keyword(s):  
Probability Distribution ◽  
Classical Method ◽  
Probability Distributions ◽  
Permutation Tests ◽  
Discrete Analog ◽  
Data Sets ◽  
Exact Probability ◽  
Discrete Probability ◽  
Discrete Probability Distributions ◽  
Independent Probability

Permutation tests are based on all possible arrangements of observed data sets. Consequently, such tests yield exact probability values obtained from discrete probability distributions. An exact nondirectional method to combine independent probability values that obey discrete probability distributions is introduced. The exact method is the discrete analog to Fisher's classical method for combining probability values from independent continuous probability distributions. If the combination of probability values includes even one probability value that obeys a sparse discrete probability distribution, then Fisher's classical method may be grossly inadequate.

scholarly journals Classroom note: An inductive derivation of Stirling numbers of the second kind and their applications in statistics

1997 ◽  
Vol 1 (2) ◽  
pp. 151-157 ◽  
Author(s):  
Anwar H. Joarder ◽  
Munir Mahmood
Keyword(s):  
Probability Distributions ◽  
Stirling Numbers ◽  
Higher Order ◽  
Inductive Method ◽  
Higher Order Moments ◽  
Discrete Probability ◽  
Discrete Probability Distributions

An inductive method has been presented for finding Stirling numbers of the second kind. Applications to some discrete probability distributions for finding higher order moments have been discussed.

INTERACTIVE SIMULATIONS OF DISCRETE PROBABILITY DISTRIBUTIONS

2019 ◽  
Author(s):  
Rubén Darío Santiago Acosta ◽  
Ernesto Manuel Hernández Cooper ◽  
Faustino Yescas Martinez
Keyword(s):  
Probability Distributions ◽  
Discrete Probability ◽  
Discrete Probability Distributions ◽  
Interactive Simulations

Risk Planning with Discrete Distribution Analysis Applied to Petroleum Spills

2013 ◽  
Vol 2 (4) ◽  
pp. 61-78 ◽  
Author(s):  
Roy L. Nersesian ◽  
Kenneth David Strang
Keyword(s):  
Probability Distribution ◽  
Probability Distributions ◽  
Low Cost ◽  
Discrete Distribution ◽  
Distribution Model ◽  
Distribution Analysis ◽  
Distribution Models ◽  
Discrete Probability ◽  
Nonparametric Statistical ◽  
Spreadsheet Software

This study discussed the theoretical literature related to developing and probability distributions for estimating uncertainty. A theoretically selected ten-year empirical sample was collected and evaluated for the Albany NY area (N=942). A discrete probability distribution model was developed and applied for part of the sample, to illustrate the likelihood of petroleum spills by industry and day of week. The benefit of this paper for the community of practice was to demonstrate how to select, develop, test and apply a probability distribution to analyze the patterns in disaster events, using inferential parametric and nonparametric statistical techniques. The method, not the model, was intended to be generalized to other researchers and populations. An interesting side benefit from this study was that it revealed significant findings about where and when most of the human-attributed petroleum leaks had occurred in the Albany NY area over the last ten years (ending in 2013). The researchers demonstrated how to develop and apply distribution models in low cost spreadsheet software (Excel).

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