Descriptive Sampling Improved
by Megdouda Tari and Abdelnasser Dahmani.
Descriptive sampling is one procedure that entails a full control over the input set of sample values. This method is based on a regular selection of the sample values and their random permutation. One of the limitation is that, in general cases, it leads to biased results in simulation. This approach is also hard to implement efficiently as it needed prior knowledge of the sample size. The paper analyses the conditions under which bias can occur and we found that the worst situation which could occur is that the response surface will have a regularity which matches the regularity of the input numbers. Based on the result of the analysis, we proposed an approach which is mainly concerned with a block of regular samples of prime size. One of the advantages is that, it eliminates the problem of the determination of the sample size beforehand. To support the proposed approach , we evaluate performance measures of a problem with wave in response.
Simulation, Descriptive sampling, Bias evaluation, Response surface, Model
Megdouda Tari, firstname.lastname@example.org
Abdelnasser Dahmani, email@example.com
Don Edwards, firstname.lastname@example.org
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