자료유형 | 학위논문 |
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서명/저자사항 | Spatial Statistics Analysis with Artificial Neural Network. |
개인저자 | Wang, Wenqian. |
단체저자명 | Northwestern University. Statistics. |
발행사항 | [S.l.]: Northwestern University., 2019. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
형태사항 | 148 p. |
기본자료 저록 | Dissertations Abstracts International 81-04A. Dissertation Abstract International |
ISBN | 9781085640695 |
학위논문주기 | Thesis (Ph.D.)--Northwestern University, 2019. |
일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Advisor: Andrews, Beth. |
이용제한사항 | This item must not be sold to any third party vendors. |
요약 | The spatial autoregressive model has been widely applied in science, in areas such as economics, public finance, political science, agricultural economics, environmental studies and transportation analyses. The classical spatial autoregressive model is a linear model for describing spatial correlation. In this work, we expand the classical model to include time lagged observations, related exogenous variables, possibly non-Gaussian, high volatility errors, and a nonlinear neural network component. The nonlinear neural network component allows for more model flexibility - the ability to learn and model nonlinear and complex relationships.We use a maximum likelihood approach for model parameter estimation. We establish consistency and asymptotic normality for these estimators under some standard conditions on the spatial/space-time model and neural network component. We investigate the quality of the asymptotic approximations for finite samples by means of numerical simulation studies.Next, we discuss the model selection in the proposed space-time autoregressive model. We employ the Shakeout noise injection method to conduct feature selection and use the likelihood ratio test for the time lag order selection. We evaluate the performance of Shakeout noise injection technique in a simulated dataset and also investigate the asymptotic approximation of the likelihood ratio test statistics by simulations.Finally, we apply our proposed spatial and space-time autoregressive models to a real world application. |
일반주제명 | Statistics. Geography. Mathematics. |
언어 | 영어 |
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