Testing for Normality in Weak Dependent Time Series

Simple item page
dc.contributor.advisorSterba-Boatwright, Blair
dc.contributor.advisorSterba-Boatwright, Blair
dc.contributor.authorJi, Hyangim
dc.contributor.authorJi, Hyangim
dc.contributor.committeeMemberJin, Lei
dc.contributor.committeeMemberGuardiola, José
dc.contributor.committeeMemberJin, Lei
dc.contributor.committeeMemberGuardiola, Jos�Jin, Lei
dc.contributor.committeeMemberGuardiola, José
dc.date.accessioned2020-04-18T03:42:45Z
dc.date.accessioned2020-04-18T03:42:45Z
dc.date.available2020-04-18T03:42:45Z
dc.date.available2020-04-18T03:42:45Z
dc.date.issued2018-12
dc.date.issued2018-122018-12
dc.description.abstractMany statistical procedures rely on the assumption of normality in the dataset being analyzed. However, many of the standard tests for normality assume independent observations. In this thesis, we look at two methods proposed to test for normality of weakly dependent data, typically time series. The bootstrap-based Anderson-Darling test (BAD) has been proposed as a powerful method to test normality in univariate data, while a technique combining information from skewness, kurtosis, and correlation has been proposed as a method to test multivariate normality in time series. In this thesis, we use simulated data to estimate Type I and Type II error rates for these methods, and compare the results with those of several ”standard” methods that assume independence. We also apply these methods to several real datasets to assess their normality.en_US
dc.description.collegeCollege of Science and Engineeringen_US
dc.description.departmentMathematics and Statisticsen_US
dc.format.extent45 pagesen_US
dc.identifier.urihttps://hdl.handle.net/1969.6/87827
dc.identifier.urihttps://hdl.handle.net/1969.6/87827https://hdl.handle.net/1969.6/87827
dc.language.isoen_USen_US
dc.rightsThis material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher.en_US
dc.titleTesting for Normality in Weak Dependent Time Seriesen_US
dc.typeTexten_US
dc.type.genreThesisen_US
thesis.degree.disciplineMathematicsen_US
thesis.degree.grantorTexas A & M University--Corpus Christien_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Ji_Hyangim_Thesis.pdf
Size:
275.1 KB
Format:
Adobe Portable Document Format
Description:
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.72 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Theses
College of Science Theses and Dissertations