Physical Sciences and Mathematics Commons^™

Estimation And Inference For Spatial And Spatio-Temporal Mixed Effects Models, Casey M. Jelsema

Dissertations

One of the most common goals of geostatistical analysis is that of spatial prediction, in other words: filling in the blank areas of the map. There are two popular methods for accomplishing spatial prediction. Either kriging, or Bayesian hierarchical models. Both methods require the inverse of the spatial covariance matrix of the data. As the sample size, n, becomes large, both of these methods become impractical. Reduced rank spatial models (RRSM) allow prediction on massive datasets without compromising the complexity of the spatial process. This dissertation focuses on RRSMs, particularly situations where the data follow non-Gaussian distributions.

The manner in …

A Geographical Approach For Integrating Belief Networks And Geographic Information Sciences To Probabilistically Predict River Depth, Nathan Lee Hopper

Dissertations

Geography is, traditionally, a discipline dedicated to answering complex spatial questions. Although spatial statistical techniques, such as weighted regressions and weighted overlay analyses, are commonplace within geographical sciences, probabilistic reasoning, and uncertainty analyses are not typical. For example, belief networks are statistically robust and computationally powerful, but are not strongly integrated into geographic information systems. This is one of the reasons that belief networks have not been more widely utilized within the environmental sciences community. Geography’s traditional method of delivering information through maps provides a mechanism for conveying probabilities and uncertainties to decision makers in a clear, concise manner. This …

A Robust Estimate For The Bifurcating Autoregressive Model With Application To Cell Lineage Data, Tamer M. E. Elbayoumi

Dissertations

The bifurcating autoregressive model (BAR) is commonly used to model binary tree data. One application for this model relates to cell lineage data in biology. The purpose of studying the cell lineage process is to know whether the observed correlations between related cells are due to similarities in the environmental, inherited effects, or a combination of both of them. Because outliers in this kind of data are quite common, the need for a robust estimation procedure is necessary. A weighted L1 (WL1) estimate for estimating the parameters of the BAR model is considered. When the weights are constant, the estimate …

A Computational Method For Estimating And Finding The Hconfidence Interval Of The Ratio Scale Parameters In The Two-Sample Problem, Mona Abdullah Alduailij

Dissertations

Testing equality of variances between two samples is applied in various fields. However, in the absence of non-normal assumptions, equality of variance tests would not yield robust results. In real life situation, the absence of such assumptions is even evident, which calls for more reliable tests to accommodate for the lack of these assumptions. There are abundant parametric and nonparametric methods for estimating the scale parameter; yet a distribution-free method for estimating and finding the confident interval ratio of scale parameters in the two-sample problem would be a reliable alternative. A comparison between existing parametric and non-parametric rank tests for …

A Comparative Study Of Exact Versus Propensity Matching Techniques Using Monte Carlo Simulation, Mukaria J. J. Itang'ata

Dissertations

Often researchers face situations where comparative studies between two or more programs are necessary to make causal inferences for informed policy decision-making. Experimental designs employing randomization provide the strongest evidence for causal inferences. However, many pragmatic and ethical challenges may preclude the use of randomized designs. In such situations, subject matching provides an alternative design approach for conducting causal inference studies. This study examined various design conditions hypothesized to affect matching procedures’ bias recovery ability.

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