{"title":"Statistical Analysis of the Beta Transmuted Standardized Half Logistic Distribution: Properties, Estimation, and Real-Data Application","authors":"P. O. Awodutire, E. C. Nduka, A. Olosunde","doi":"10.9734/ajpas/2023/v25i2554","DOIUrl":null,"url":null,"abstract":"In this paper, a new distribution called the Beta Transmuted Half Logistic Distribution is derived and studied using the Beta Transmuted-G distribution. The distribution generalizes the half logistic distribution for more flexibility. Then expressions for the moments, moment generating function, order statistic, survival function, and the hazard function were studied. Estimation of parameters of the model was done using the maximum likelihood estimation approach. Simulation studies were conducted to assess the performance of the estimates of the parameter. Furthermore, the distribution is applied to a real-life dataset, demonstrating its superior performance in terms of producing a better fit compared to its submodels. This research contributes to the field of probability distributions and provides a valuable tool for modeling and analyzing various types of data.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":"8 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Probability and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9734/ajpas/2023/v25i2554","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a new distribution called the Beta Transmuted Half Logistic Distribution is derived and studied using the Beta Transmuted-G distribution. The distribution generalizes the half logistic distribution for more flexibility. Then expressions for the moments, moment generating function, order statistic, survival function, and the hazard function were studied. Estimation of parameters of the model was done using the maximum likelihood estimation approach. Simulation studies were conducted to assess the performance of the estimates of the parameter. Furthermore, the distribution is applied to a real-life dataset, demonstrating its superior performance in terms of producing a better fit compared to its submodels. This research contributes to the field of probability distributions and provides a valuable tool for modeling and analyzing various types of data.