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Annals of Data Science

Erschienen in:

05.05.2018

A New Generalized Class of Distributions: Properties and Estimation Based on Type-I Censored Samples

verfasst von: Zubair Ahmad

Erschienen in: Annals of Data Science | Ausgabe 2/2020

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Abstract

This article introduces a new generalized family of distributions, which is a generalization of the exponentiated and transmuted family of distributions. A special model of this family, namely, new generalized Weibull distribution is considered in detail. General expressions for the mathematical properties of the proposed family are derived. Maximum likelihood estimates of the unknown parameters are obtained. A simulation study is done to evaluate the performances of the maximum likelihood estimators. Furthermore, estimation based on Type-I censored samples is also discussed. Finally, the superiority of the new proposal is illustrated empirically by analyzing a real-life application.

Vorheriger Artikel Performance of Some Factor Analysis Techniques

Nächster Artikel Maximum Product Spacing Estimation of Weibull Distribution Under Adaptive Type-II Progressive Censoring Schemes

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Titel: A New Generalized Class of Distributions: Properties and Estimation Based on Type-I Censored Samples
verfasst von: Zubair Ahmad
Publikationsdatum: 05.05.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Annals of Data Science / Ausgabe 2/2020
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-018-0160-5

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