Please use this identifier to cite or link to this item: http://prr.hec.gov.pk/jspui/handle/123456789/11329
 Title: New Generalizations of Power Cauchy Distribution Authors: Zubair, Muhammad Keywords: Statistics Issue Date: 2018 Publisher: Islamia University, Bahawalpur. Abstract: Four new generalizations of power-Cauchy distribution are proposed in this thesis. Firstly, the Poisson-X family is proposed and its important mathematical properties are obtained. Then, a member of the Poisson-X family namely the Poisson power-Cauchy)distributionisdeﬁnedanditsstructuralpropertiesareinvestigated. The proposed model is ﬁtted to three real-life data sets to illustrate its ﬂexibility. Secondly, the log-odd normal generalized family of distributions is introduced. Then, a special model of this family, the log-odd normal power-Cauchy is deﬁned and its mathematical properties are obtained. A real-life data set is used to prove the superiority of the proposed model. Thirdly, the Weibull-Power-Cauchy distribution is proposed and its mathematical properties are obtained. Two useful characterizations based on truncated moments are also presented. The proposed model is applied to three real-life data sets to investigate its ﬂexibility. Lastly, a new extensionofpower-Cauchymodelisproposedbycompoundingthepower-Cauchyand negative-binomial distribution called the power-Cauchy negative-binomial distribution. Some mathematical properties of the proposed model are obtained and the model parameters are estimated using the maximum likelihood method. A simulation study is carried out to investigate the performance of maximum likelihood method. The ﬂexibility of the proposed model is illustrated through three real-life data sets. Then, a new class of regression model is introduced for location and scale based on the logarithm of the proposed random variable and, estimation and inference on the regression coefﬁcients are discussed. Gov't Doc #: 18683 URI: http://prr.hec.gov.pk/jspui/handle/123456789/11329 Appears in Collections: PhD Thesis of All Public / Private Sector Universities / DAIs.

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