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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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Bilateral Matching Method for Business Resources Based on Synergy Effects and Incomplete Data.

Shuhai Wang1,2, Linfu Sun1,2, Yang Yu3

  • 1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China.

Entropy (Basel, Switzerland)
|August 29, 2024
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Summary

This study introduces a novel bilateral matching method to help businesses find valuable resources on cloud platforms, even with incomplete data. The approach optimizes resource allocation by maximizing satisfaction for both suppliers and demanders, enhancing synergy effects.

Keywords:
bilateral matchingbusiness resourcesdata analyticsentropy weight methodfuzzy analytic hierarchy processsynergy effect

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Area of Science:

  • Computer Science
  • Information Systems
  • Business Analytics

Background:

  • Enterprises face challenges in identifying high-quality business resources from vast information on third-party cloud platforms.
  • Incomplete data often hinders accurate resource matching and allocation.

Purpose of the Study:

  • To propose a bilateral matching method for business resources that accounts for synergy effects and incomplete data.
  • To enhance the accuracy and value of resource acquisition for enterprises on cloud platforms.

Main Methods:

  • Utilized k-nearest neighbor imputation for handling missing values based on comprehensive similarity.
  • Developed a satisfaction evaluation index system for suppliers and demanders.
  • Determined index weights using the fuzzy analytic hierarchy process (FAHP) and entropy weighting method (EWM).
  • Constructed a bilateral matching model to maximize mutual satisfaction and synergy.
  • Solved the model using the linear weighting method.

Main Results:

  • The proposed method effectively fills missing data, improving resource matching accuracy.
  • The bilateral matching model successfully maximizes satisfaction for both suppliers and demanders.
  • Demonstrated enhanced synergy effects in business resource allocation.
  • Verified effectiveness through practical application and comparative experiments.

Conclusions:

  • The developed bilateral matching method provides an effective solution for enterprises seeking valuable business resources on cloud platforms.
  • The integration of synergy effects and incomplete data handling significantly improves resource allocation outcomes.
  • The method offers a robust framework for optimizing business resource acquisition in complex data environments.