{"title":"On Some Aspects of a Generalized Asymmetric Normal Distribution","authors":"C. Kumar, G. V. Anila","doi":"10.6092/ISSN.1973-2201/7134","DOIUrl":null,"url":null,"abstract":"The normal and skew normal distributions are not adequate enough for modeling plurimodal data situations. In order to overcome this drawback of normal and skew normal distribution, Kumar and Anusree (2011) proposed a new class of distribution namely \"the generalized mixture of standard normal and skew normal distributions (GMNSND)\". In this paper we consider an extended version of the GMNSND as a wide class of plurimodal asymmetric normal distribution and investigate some of its important distributional properties. Location-scale extension of the proposed model is also defined and discussed the estimation of its parameters by method of maximum likelihood. Further, four real life data sets are considered for illustrating the usefulness of this model.","PeriodicalId":45117,"journal":{"name":"Statistica","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2018-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.6092/ISSN.1973-2201/7134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
The normal and skew normal distributions are not adequate enough for modeling plurimodal data situations. In order to overcome this drawback of normal and skew normal distribution, Kumar and Anusree (2011) proposed a new class of distribution namely "the generalized mixture of standard normal and skew normal distributions (GMNSND)". In this paper we consider an extended version of the GMNSND as a wide class of plurimodal asymmetric normal distribution and investigate some of its important distributional properties. Location-scale extension of the proposed model is also defined and discussed the estimation of its parameters by method of maximum likelihood. Further, four real life data sets are considered for illustrating the usefulness of this model.