Wilson Score Kernel Density Estimation for Bernoulli Trials

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Abstract

We propose a new function estimator, called Wilson Score Kernel Density Estimation, that allows to esti-mate a mean probability and the surrounding confidence interval for parameterized processes with binomiallydistributed outcomes. Our estimator combines the advantages of kernel smoothing, from Kernel Density Esti-mation, and robustness to low number of samples, from Wilson Score. This allows for more robust and dataefficient estimates compared to the individual use of these two estimators. While our estimator is generallyapplicable for processes with binomially distributed outcomes, we will present it in the context of iterativeoptimization. Here we first show the advantage of our estimator on a mathematically well defined problem,and then apply our estimator to an industrial automation process.
Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Informatics in Control, Automation and Robotics
EditorsOleg Gusikhin, Kurosh Madani, Janan Zaytoon
Volume1
PublisherSCITEPRESS Digital Library
Publication date10. Jul 2020
Pages305-313
ISBN (Electronic)978-989-758-442-8
DOIs
Publication statusPublished - 10. Jul 2020
Event17th International Conference on Informatics in Control, Automation and Robotics (ICINCO) -
Duration: 7. Jul 20209. Jul 2020

Conference

Conference17th International Conference on Informatics in Control, Automation and Robotics (ICINCO)
Period07/07/202009/07/2020

Keywords

  • Iterative Learning, Statistical Function Estimator s, Binomial Trials
  • Statistical function estimators
  • Binomial trials
  • Iterative learning

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