The purpose of this dissertation is to study the first order autoregressive model in the spatial context with specific error structures. We begin by supposing that the error structure has a long memory in both the i and the j components. Whenever the model parameters alpha and beta equal one, the limiting distribution of the sequence of normalized Fourier coefficients of the spatial process is shown to be a function of a two parameter fractional Brownian sheet. This result is used to find the limiting distribution of the periodogram ordinate of the spatial process under the null hypothesis that alpha equals one and beta equals one. We then give the limiting distribution of the normalized Fourier coefficients of the spatial process for both a moving average and autoregressive error structure. Two cases of autoregressive errors are considered. The first error model is autoregressive in one component and the second is autoregressive in both components. We show that the normalizing factor needed to ensure convergence in distribution of the sequence of Fourier coefficients is different in the moving average case, and the two autoregressive cases. In other words, the normalizing factor differs in each of these three cases. Finally, a specific case of the functional central limit theorem in the spatial setting is stated and proved. The assumptions made here are placed on the autocovariance functions. We then discuss some specific examples and provide a test statistics based on the periodogram ordinate.
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Doctor of Philosophy (Ph.D.)
College of Sciences
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Adu, Nathaniel, "Spatial Models with Specific Error Structures" (2019). Electronic Theses and Dissertations. 6785.
Restricted to the UCF community until December 2020; it will then be open access.