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But degradation data that reflect a product's inherent randomness of degradation are often contaminated by measurement errors. To deal with the problem, this paper proposes a Wiener\u2010based model with an assumption of logistic distributed measurement errors and adopts the Monte Carlo expectation\u2010maximization method together with the Gibbs sampling for parameter estimation. Based on the model and parameter estimates, an efficient algorithm is proposed for a quick calculation of maximum likelihood value. Also, the estimation of remaining useful lifetime is discussed. Simulation results show that the proposed model is relatively better and more robust in comparison with the Wiener process with Gaussian noises. Finally, the application of the proposed model is illustrated by an example.<\/jats:p>","DOI":"10.1002\/qre.2325","type":"journal-article","created":{"date-parts":[[2018,5,25]],"date-time":"2018-05-25T06:55:31Z","timestamp":1527231331000},"page":"1289-1303","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Wiener\u2010based degradation model with logistic distributed measurement errors and remaining useful life estimation"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3140-1937","authenticated-orcid":false,"given":"Yan","family":"Shen","sequence":"first","affiliation":[{"name":"Department of Statistics, School of Economics Xiamen University Xiamen China"}]},{"given":"Lijuan","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Industrial Systems Engineering and Management National University of Singapore 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