Theses and Dissertations from UMD

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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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    Comparing Regime-Switching Models In Time Series: Logistic Mixtures vs. Markov Switching
    (2007-05-16) Paliouras, Dimitrios V.; Kedem, Benjamin; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The purpose of this thesis is to review several related regime-switching time series models. Specifically, we use simulated data to compare models where the unobserved state vector follows a Markov process against an independent logistic mixture process. We apply these techniques to crude oil and heating oil futures prices using several explanatory variables to estimate the unobserved regimes. We find that crude oil is characterized by regime switching, where prices alternate between a high volatility state with low returns and significant mean reversion and a low volatility state with positive returns and some trending. The spread between one-month and three-month futures prices is an important determinant in the dynamics of crude oil prices.
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    Factor Analysis of Cross-Classified Data
    (2005-08-04) Tsou, Hsiao-Hui Sophie; Slud, Eric V; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This thesis introduces a model hierarchy related to Principal Component Analysis and Factor Analysis, in which vector measurements are linearly decomposed into a relatively small set of hypothetical principal directions, for purposes of dimension reduction. The mathematical specification of unknown parameters in the models is unified. Identifiability of the suitably defined models is proved. The EM algorithm and the Newton-Raphson algorithm based on likelihoods and profile likelihoods are implemented to get computationally effective (maximum likelihood) estimators for the unknown parameters. A restricted model (with some error variances $0$) and a sufficient condition for a local maximum likelihood estimate are established. Score tests are constructed to check whether error variances are $0$, which is shown to be associated with non-identifiability of models. Statistical tests of goodness of fit of the models to data are established in a likelihood ratio testing framework, so that the most parsimoniously parameterized model consistent with the data can be chosen for purposes of description and classification of the experimental settings. The results are applied on a real data set involving coronal cross-sectional ultrasound pictures of the human tongue surface during speech. The likelihood ratio test is used to test the fit of the PARAFAC model on the real coronal tongue data, leading to a finding of inadequacy of the PARAFAC model.