{"title":"离散Pranav分布及其应用","authors":"Berhane Abebe, K. Shukla","doi":"10.15406/bbij.2019.08.00267","DOIUrl":null,"url":null,"abstract":"In many cases, it is not easy to get samples from continuous distributions. The observed values, in the most of cases, are collected actually discrete in nature for the reason that they are measured to only finite number of decimal places and cannot completely presents all points in a continuum. According to Lai,1 discretization of a continuous lifetime model is an appealing approach to derive a discrete lifetime model corresponding to the continuous one. Therefore, it is reasonable and convenient to model the situation by an appropriate discrete distribution generated from the underlying continuous distribution preserving one or more important characteristics including probability density function (pdf), mean residual life function etc. and important statistical properties of the distribution.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A discrete Pranav distribution and its applications\",\"authors\":\"Berhane Abebe, K. Shukla\",\"doi\":\"10.15406/bbij.2019.08.00267\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In many cases, it is not easy to get samples from continuous distributions. The observed values, in the most of cases, are collected actually discrete in nature for the reason that they are measured to only finite number of decimal places and cannot completely presents all points in a continuum. According to Lai,1 discretization of a continuous lifetime model is an appealing approach to derive a discrete lifetime model corresponding to the continuous one. Therefore, it is reasonable and convenient to model the situation by an appropriate discrete distribution generated from the underlying continuous distribution preserving one or more important characteristics including probability density function (pdf), mean residual life function etc. and important statistical properties of the distribution.\",\"PeriodicalId\":90455,\"journal\":{\"name\":\"Biometrics & biostatistics international journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biometrics & biostatistics international journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15406/bbij.2019.08.00267\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biometrics & biostatistics international journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15406/bbij.2019.08.00267","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A discrete Pranav distribution and its applications
In many cases, it is not easy to get samples from continuous distributions. The observed values, in the most of cases, are collected actually discrete in nature for the reason that they are measured to only finite number of decimal places and cannot completely presents all points in a continuum. According to Lai,1 discretization of a continuous lifetime model is an appealing approach to derive a discrete lifetime model corresponding to the continuous one. Therefore, it is reasonable and convenient to model the situation by an appropriate discrete distribution generated from the underlying continuous distribution preserving one or more important characteristics including probability density function (pdf), mean residual life function etc. and important statistical properties of the distribution.