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  • 标题:On the Extended Generalized Inverted Kumaraswamy Distribution
  • 本地全文:下载
  • 作者:Qasim Ramzan ; Sadia Qamar ; Muhammad Amin
  • 期刊名称:Computational Intelligence and Neuroscience
  • 印刷版ISSN:1687-5265
  • 电子版ISSN:1687-5273
  • 出版年度:2022
  • 卷号:2022
  • DOI:10.1155/2022/1612959
  • 语种:English
  • 出版社:Hindawi Publishing Corporation
  • 摘要:In this work, we provide a new generated class of models, namely, the extended generalized inverted Kumaraswamy generated (EGIKw-G) family of distributions. Several structural properties (survival function sf, hazard rate function hrf, reverse hazard rate function rhrf, quantile function qf and median, sth raw moment, generating function, mean deviation md, etc.) are provided. The estimates for parameters of new G class are derived via maximum likelihood estimation MLE method. The special models of the proposed class are discussed, and particular attention is given to one special model, the extended generalized inverted Kumaraswamy Burr XII (EGIKw-Burr XII) model. Estimators are evaluated via a Monte Carlo simulation MCS. The superiority of EGIKw-Burr XII model is proved using a lifetime data applications.
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