Inference for Dependent Generalized Extreme Values

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atmire.migration.oldid4773
dc.contributor.advisorChen, Gemai
dc.contributor.authorHe, Jialin
dc.contributor.committeememberLu, Xuewen
dc.contributor.committeememberSun, Bingrui
dc.date.accessioned2016-08-23T15:57:36Z
dc.date.available2016-08-23T15:57:36Z
dc.date.issued2016
dc.date.submitted2016en
dc.description.abstractThe Generalized Extreme Value (GEV) distribution is the most commonly used distribution for analyzing extreme values. However, the existing GEV models are based on the assumption that the extreme values are independent, which is sometimes not the case in real data analysis. This thesis aims to overcome this issue by bringing forward a new GEV model that considers the correlation between two successive extreme values. The proposed model can be applied to both independent and dependent extreme values. The point estimation and interval estimation methods for the model parameters are introduced. Simulation studies describe the estimation performance under different combinations of parameters and show that the proposed methods have better performance than the traditional GEV model. Moreover, a study of the Average Run Length (ARL) for the GEV model is conducted through simulation. In the end, two real data analyses are included to illustrate the application of our methodology.en_US
dc.identifier.citationHe, J. (2016). Inference for Dependent Generalized Extreme Values (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26513en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/26513
dc.identifier.urihttp://hdl.handle.net/11023/3208
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectStatistics
dc.subject.classificationExtreme Eventsen_US
dc.subject.classificationExtreme Value Theoryen_US
dc.subject.classificationGeneralized Extreme Value Modelen_US
dc.subject.classificationAutoregressive Processen_US
dc.titleInference for Dependent Generalized Extreme Values
dc.typemaster thesis
thesis.degree.disciplineMathematics and Statistics
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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