<p>In many spatio-temporal applications a vector of covariates is measured alongside a spatio-temporal response. In such cases, the purpose of the statistical model is to quantify the change, in expectation or otherwise, in the response due to a change in the predictors while adequately accounting for the spatio-temporal structure of the response, the predictors, or both. The most common approach for building such a model is to confine the relationship between the response and the predictors to a single spatio-temporal coordinate. For spatio-temporal problems, however, the relationship between the response and predictors may not be so confined. For example, spatial models are often used to quantify the effect of pollution exposure on mortality. Yet, an unknown lag exists between time of exposure to pollutants and mortality. Furthermore, due to mobility and atmospheric movement, a spatial lag between pollution concentration and mortality may also exist (e.g. subjects may live in the suburbs where pollution levels are low but work in the city where pollution levels are high).</p><p>The contribution of this thesis is to propose a hierarchical modeling framework which captures complex spatio-temporal relationships between responses and covariates. Specifically, the models proposed here use kernels to capture spatial and/or temporal lagged effects. Several forms of kernels are proposed with varying degrees of complexity. In each case, however, the kernels are assumed to be parametric with parameters that are easily interpretable and estimable from the data. Full distributional results are given for the Gaussian setting along with consequences of model misspecification. The methods are shown to be effective in understanding the complex relationship between responses and covariates through various simulated examples and analyses of physical data sets.</p> / Dissertation

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This study investigates the driving forces of political participation in the Netherlands from a perspective of political equality. Following an article by Jan Teorell (2006) a procedural approach of political equality is examined by studying whether resources or incentives make up the primary driving forces of political participation. An equality hypothesis is derived from normative democratic theory perspectives of participation. The hypothesis is divided in two parts. 1a. and 1b. state that resources should have no direct impact on political participation and incentives should have a direct impact on political participation. Hypotheses 2.a. and 2.b. state that resources do not have an impact on incentives known to impact political participation and that incentives have an impact on resources known to impact political participation. The Dutch panel data LISS (Longitudinal Internet Studies for the Social Sciences) is used and cross-lagged effect models test the hypotheses. The panel data approach improves on previous research in that it ensures the correct temporal order of cause and effect. This helps us better understand the causal relationships at hand compared to previous cross-sectional approaches in the literature. This also enables us to disentangle the effects of all different factors on each other. The results show that while incentives do have some effect on political participation, resources remain significant when controlling for incentives. Furthermore, the results show that resources and incentives are tangled and have several reciprocal relationships with each other as well as political participation itself. In light of these findings, it is concluded that opportunities for political participation are not equal in the Netherlands, despite it being a least-likely case for political inequality. Additionally, the relationships between resources, incentives and political participation in relation to political equality are more complex than normally assumed in the literature.