Browsing by Subject "Spatial analysis (Statistics)"
Now showing 1 - 7 of 7
Results Per Page
Sort Options
Item Correlation properties of diffusers for multiplex holography(Texas Tech University, 1979-08) Kral, Edward LeeA promising method of representing two-dimensional space-variant optical systems is through multiplex holography. The multiplexing operation requires the use of diffusers in the reference beam path to provide a unique code for each object wave. In this thesis, various types of diffusers are analyzed and compared within the framework of multiplex holography. A simple model is initially developed which can accommodate a wide range of diffuser families, including pure phase, pure amplitude, and combined amplitude and phase diffusers. Crosscorrelation and autocorrelation calculations are then presented for both spherical wave (chirp) illumination and plane wave illumination, and a signal-to-noise analysis based on these calculations is included. Finally, the implications of the analysis are thoroughly discussed, and recommendations for further research are given.Item Information heterogeneity and voter uncertainty in spatial voting: the U.S. presidential elections, 1992-2004(2007-12) Lee, So Young; Hinich, Melvin J.; Lin, Tse-minThis dissertation addresses voters' information heterogeneity and its effect on spatial voting. While most spatial voting models simply assume that voter uncertainty about candidate preferences is homogeneous across voters despite Downs' early use of uncertainty scale to classify the electorate, information studies have discovered that well and poorly informed citizens have sizeable and consistent differences in issue conceptualization, perception, political opinion and behavior. Built upon the spatial theory's early insights on uncertainty and the findings of information literature, this dissertation claims that information effects should be incorporated into the spatial voting model. By this incorporation, I seek to unify the different scholarly traditions of the spatial theory of voting and the study of political information. I hypothesize that uncertainty is not homogeneous, but varies with the level of information, which are approximated by political activism as well as information on candidate policy positions. To test this hypothesis, I employ heteroskedastic probit models that specify heterogeneity of voter uncertainty in probabilistic models of spatial voting. The models are applied to the U.S. presidential elections in 1992-2004. The empirical results of the analysis strongly support the expectation. They reveal that voter uncertainty is heterogeneous as a result of uneven distributions of information and political activism even when various voting cues are available. This dissertation also discovers that this heterogeneity in voter uncertainty has a significant effect on electoral outcomes. It finds that the more uncertain a voter is about the candidates, the more likely he or she is to vote for the incumbent or a better-known candidate. This clearly reflects voters' risk-averse attitudes that reward the candidate with greater certainty, all other things held constant. Heterogeneity in voter uncertainty and its electoral consequences, therefore, have important implications for candidates' strategies. The findings suggest that the voter heterogeneity leads candidates' equilibrium strategies and campaign tactics to be inconsistent with those that spatial analysts have normally proposed.Item Investigation of bootstrap estimates of the parameters, their standard errors, and associated confidence intervals of structural equation models with ordered categorical variables(Texas Tech University, 1999-12) Fafouti, ElisabethThis study investigates the performance of the bootstrap when it is applied to structural equation models with ordered categorical variables. The study focuses on the parameter estimates, their standard errors and the coverage rates of the associated bootstrap confidence intervals. Structural equation models are used widely in many disciplines and often the data analyzed involve ordered categorical variables. The performance of the bootstrap has been investigated through simulation, and it is also compared with the Maximum Likelihood estimator applied on both polychoric correlation matrices and Pearson's product moment correlation matrices. The bootstrap samples are generated randomly and transformed, so that they preserve the covariance structure of the model. Then the polychoric correlation matrix is computed and analyzed for each sample. The study involves three different models, and for each model different sample sizes have been analyzed. One of the models that has been analyzed is one that Muthen and Kaplan used in their research to investigate the performance of the Categorical Variable Methodology (CVM) estimator, so direct comparisons between the two methods have been made. The bootstrap compares well with the CVM estimator. The results of this research indicate that the bootstrap pro\ ides correct standard errors that are larger than the standard errors obtained from the Maximum Likelihood estimator when it is applied on the Pearson's product moment correlation matrices. The coverage rates of the bootstrap confidence intervals have also been investigated, using two methods: the percentile method and the bootstrap-t method. The results are not very encouraging, especially for the bootstrap-t method, since the coverage rates are in some cases far away from the prespecified confidence level. The percentile method seems to perform better than the bootstrap-t method with regard to coverage rates, though it presents problems also. The performance of the bootstrap is affected by the sample size, the complexity of the model and the parameter values. Overall, the bootstrap performs rather adequately and could provide a valid alternative to other estimation methods for structural equation models if the researchers are cautious on its application.Item Spatial analysis of playas on the Texas High Plains(Texas Tech University, 2004-12) Quillin, John PNot availableItem Spatial analysis of surface collected materials at the Lubbock Lake Landmark utilizing geographic information systems(Texas Tech University, 2002-08) Gill, Matthew INot availableItem Spatial modelling and analysis of wireless ad-hoc and sensor networks: an energy perspective(2006) Baek, Seung Jun; De Veciana, GustavoItem Toward a comprehensive, unified, framework for analyzing spatial location choice(2005) Sivakumar, Aruna; Bhat, Chandra R. (Chandrasekhar R.), 1964-In today’s world of increasing congestion and insufficient scope for infrastructural expansions, urban and transportation planners rely on the accuracy and behavioral realism of travel demand models to make informed policy decisions. The development of accurate and behaviorally realistic travel demand models requires a good understanding of individual travel behavior, and an important step toward this has been the development of the activity-based paradigm, which states that travel is a result of the desire to participate in activities at spatially scattered locations. Activity-based travel demand modeling systems essentially model the activity-travel patterns of individuals, which are characterized by several attributes such as activity purpose, location of activity participation and choice of mode. Of all these attributes, the choice of location of activity participation is one that has received relatively inadequate attention in the literature. On the other hand, the location of activity participation spatially pegs the daily activity-travel patterns of individuals. Accurate predictions of activity location are, therefore, key to effective travel demand management and air quality control strategies. Moreover, an understanding of the factors that influence the choice of location can contribute to more effective land-use and zoning policies. The broad objectives of this dissertation research are two-fold. The first objective is to develop a comprehensive econometric model of location choice for non-work activities that incorporates accuracy and behavioral realism in capturing different choice behaviors. This was achieved through the comprehensive introduction of heterogeneity in choice behavior, including observed and unobserved sources of inter- and intra-personal heterogeneity, spatial correlation, variety seeking and loyalty/inertial behavior, and spatial cognition. The estimation of such a flexible model typically requires the use of simulated maximum likelihood estimation (SMLE). The second broad objective of this research is to contribute toward improving the efficiency of the SMLE by comparing the performance of various quasi-Monte Carlo (QMC) sequences and their scrambled versions. Numerical experiments were designed and the Random Linear and Random Digit Scrambled Faure sequences are identified as the most efficient. Finally, all these research efforts contribute to the empirical estimation of non-maintenance shopping location choice models using panel data from the Mobidrive survey.