Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/18844
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dc.titleMultistep Yule-walker estimation of autoregressive models
dc.contributor.authorYOU TINGYAN
dc.date.accessioned2010-12-31T18:01:09Z
dc.date.available2010-12-31T18:01:09Z
dc.date.issued2010-07-26
dc.identifier.citationYOU TINGYAN (2010-07-26). Multistep Yule-walker estimation of autoregressive models. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/18844
dc.description.abstractThe aim of this work is to fit a ?wrong? model to an observed time series by employing higher order Yule-Walker equations in order to enhance the fitting accuracy. Several parameter estimation methods for autoregressive models were reviewed, such as Maximum Likelihood method, Least Square method, Yule-Walker method, Burg?method, etc. Comparison of the estimation accuracy between the well-known Yule-Walker method and our new multistep Yule-Walker method based on the autocorrelation function (ACF) is made. The effect of different number of Yule-Walker equations on the estimation performance is investigated. Monte Carlo analysis and real data are used to check the performance of the proposed method.
dc.language.isoen
dc.subjectTime series, Autoregressive Model, Least Square method, Yule-Walker Method, ACF
dc.typeThesis
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.contributor.supervisorXIA YINGCUN
dc.description.degreeMaster's
dc.description.degreeconferredMASTER OF SCIENCE
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Master's Theses (Open)

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