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AuthorNik, Ali Saadati
AuthorAsgharzadeh, Akbar
AuthorBaklizi, Ayman
Available date2023-11-06T08:19:07Z
Publication Date2023-01-01
Publication NameStatistics, Optimization and Information Computing
Identifierhttp://dx.doi.org/10.19139/soic-2310-5070-1591
CitationNik, A. S., Asgharzadeh, A., & Baklizi, A. (2023). Inference Based on New Pareto-Type Records With Applications to Precipitation and Covid-19 Data. Statistics, Optimization & Information Computing, 11(2), 243-257.‏
ISSN2311004X
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85152413713&origin=inward
URIhttp://hdl.handle.net/10576/49049
AbstractWe consider estimation and prediction of future records based on observed records from the new Pareto type distribution proposed recently by Bourguignon et al. (2016), ¡°M. Bourguignon, H. Saulo, R. N. Fernandez, A new Pareto-type distribution with applications in reliability and income data, Physica A, 457 (2016), 166-175¡±. We first obtain the maximum likelihood and Bayesian estimators of the model parameters. We then derive several point predictors for a future record on the basis of the first n observed records. Two real data sets on precipitation and Covid 19 are analysed and a Monte Carlo simulation study has been performed to evaluate the statistical performance of point predictors presented in this paper
SponsorWe are grateful to the editor and reviewer for their valuable comments and suggestions in revising this paper
Languageen
PublisherInternational Academic Press
SubjectEstimation
Pareto distribution
Prediction
Records
Simulation
TitleInference Based on New Pareto-Type Records With Applications to Precipitation and Covid-19 Data
TypeArticle
Pagination243-257
Issue Number2
Volume Number11
dc.accessType Open Access


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