Dissertation
Maximum likelihood estimation of an unknown change-point in the parameters of a multivariate Gaussian series with applications to environmental monitoring
Washington State University
Doctor of Philosophy (PhD), Washington State University
05/2010
DOI:
https://doi.org/10.7273/000006086
Abstract
The computable expressions for the asymptotic distribution of the change-point maximum likelihood estimator (mle) were derived when a change occurred in the mean and covariance matrix at an unknown point of a sequence of independently distributed multivariate Gaussian series. The derivation was based on ladder heights of Gaussian random walks hitting the half-line. We then demonstrated that change in a single parameter or change-point analysis in a univariate series can be derived as special cases. A simulation study was carried out to investigate the robustness of the asymptotic distribution to departure from normality, the sample size, location of change-point and amount of change under the multivariate and univariate case. The comparison of the asymptotic mle with Cobb's conditional MLE and Bayesian estimation method using non-informative prior and conjugate prior was also carried out in the simulation study. The asymptotic distribution of the change-point mle was used to compute the confidence interval of the change-point of the stream flows at Northern Quebec Labrador Region and zonal annual mean temperature deviations.
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Details
- Title
- Maximum likelihood estimation of an unknown change-point in the parameters of a multivariate Gaussian series with applications to environmental monitoring
- Creators
- Pengyu Liu
- Contributors
- Venkata Krishna Jandhyala (Chair)Stergios B Fotopoulos (Committee Member) - Washington State University, Department of Finance and Management ScienceNairanjana Dasgupta (Committee Member) - Washington State University, Department of Mathematics and Statistics
- Awarding Institution
- Washington State University
- Academic Unit
- Department of Mathematics and Statistics
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Publisher
- Washington State University
- Number of pages
- 266
- Identifiers
- 99901055121701842
- Language
- English
- Resource Type
- Dissertation