Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

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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Subject: search.filters.subject.Generalized Additive Model

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    A NOVEL MEASUREMENT OF JOB ACCESSIBILITY BASED ON MOBILE DEVICE LOCATION DATA
    (2022) Zhao, Guangchen; Zhang, Lei LZ; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Mobile device location data (MDLD) can offer a new perspective on measuring accessibility. Compared with the traditional accessibility measures, MDLD is capable of capturing people’s preferences with the observed locations. This study proposes a job accessibility measure based on the identified home and work locations from MDLD, evaluating the job accessibility by the proportion of workers identified working in zones within a certain time threshold. In the case study on the Baltimore region, the job accessibility from the MDLD-based measure is compared with the results from a widely-used traditional measure. Then, generalized additive models (GAM) are built to analyze the socio-demographic impact on job accessibility from a MDLD-based measure and a traditional measure, with a feature-to-feature comparison. Finally, the socio-demographic characteristics of regions where there are major disparities between the job accessibility from the traditional measure and the MDLD-based measure are also evaluated from the Student's t-test results.
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    Nonparametric Estimation and Testing of Interaction in Generalized Additive Models
    (2011) Li, Bo; Smith, Paul J; Mathematics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The additive model overcomes the "curse of dimensionality" in general nonparametric regression problems, in the sense that it achieves the optimal rate of convergence for a one-dimensional smoother. Meanwhile, compared to the classical linear regression model, it is more flexible in defining an arbitrary smooth functional relationship between the individual regressor and the conditional mean of the response variable Y given X. However, if the true model is not additive, the estimates may be seriously biased by assuming the additive structure. In this dissertation, generalized additive models (with a known link function) are considered when containing second order interaction terms. We present an extension of the existing marginal integration estimation approach for additive models with the identity link. The corresponding asymptotic normality of the estimators is derived for the univariate component functions and interaction functions. A test statistic for testing significance of the interaction terms is developed. We obtained the asymptotics for the test functional and local power results. Monte Carlo simulations are conducted to examine the finite sample performance of the estimation and testing procedures. We code our own local polynomial pre-smoother with fixed bandwidths and apply it in the integration method. The widely used LOESS function with fixed spans is also used as a pre-smoother. Both methods provide comparable results in estimation and are shown to work well with properly chosen smoothing parameters. With a small and moderate sample size, the implementation of the test procedure based on the asymptotics may produce inaccurate results. Hence a wild bootstrap procedure is provided to get empirical critical values for the test. The test procedure performs well in fitting the correct quantiles under the null hypothesis and shows strong power against the alternative.