{"title":"一种新的Poisson广义Lindley回归模型","authors":"Yupapin Atikankul","doi":"10.17713/ajs.v52i1.1344","DOIUrl":null,"url":null,"abstract":"In this paper, a new count distribution is introduced. It is a mixture of the Poisson and generalized Lindley distributions. Statistical properties of the proposed distribution including the factorial moments, probability generating function, moment generating function and moments are studies. Maximum likelihood estimators of unknown parameters are derived. Moreover, an alternative count regression model based on the proposed distribution is presented. Finally, the proposed model is applied for real data and compared with other well-known models.","PeriodicalId":51761,"journal":{"name":"Austrian Journal of Statistics","volume":"22 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Poisson Generalized Lindley Regression Model\",\"authors\":\"Yupapin Atikankul\",\"doi\":\"10.17713/ajs.v52i1.1344\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new count distribution is introduced. It is a mixture of the Poisson and generalized Lindley distributions. Statistical properties of the proposed distribution including the factorial moments, probability generating function, moment generating function and moments are studies. Maximum likelihood estimators of unknown parameters are derived. Moreover, an alternative count regression model based on the proposed distribution is presented. Finally, the proposed model is applied for real data and compared with other well-known models.\",\"PeriodicalId\":51761,\"journal\":{\"name\":\"Austrian Journal of Statistics\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Austrian Journal of Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17713/ajs.v52i1.1344\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Austrian Journal of Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17713/ajs.v52i1.1344","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
A New Poisson Generalized Lindley Regression Model
In this paper, a new count distribution is introduced. It is a mixture of the Poisson and generalized Lindley distributions. Statistical properties of the proposed distribution including the factorial moments, probability generating function, moment generating function and moments are studies. Maximum likelihood estimators of unknown parameters are derived. Moreover, an alternative count regression model based on the proposed distribution is presented. Finally, the proposed model is applied for real data and compared with other well-known models.
期刊介绍:
The Austrian Journal of Statistics is an open-access journal (without any fees) with a long history and is published approximately quarterly by the Austrian Statistical Society. Its general objective is to promote and extend the use of statistical methods in all kind of theoretical and applied disciplines. The Austrian Journal of Statistics is indexed in many data bases, such as Scopus (by Elsevier), Web of Science - ESCI by Clarivate Analytics (formely Thompson & Reuters), DOAJ, Scimago, and many more. The current estimated impact factor (via Publish or Perish) is 0.775, see HERE, or even more indices HERE. Austrian Journal of Statistics ISNN number is 1026597X Original papers and review articles in English will be published in the Austrian Journal of Statistics if judged consistently with these general aims. All papers will be refereed. Special topics sections will appear from time to time. Each section will have as a theme a specialized area of statistical application, theory, or methodology. Technical notes or problems for considerations under Shorter Communications are also invited. A special section is reserved for book reviews.