Thesis (Ph.D.)--University of Alabama at Birmingham, 2007. / Title from PDF title page (viewed on Feb. 19, 2010). Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 1226-131).

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 174-179).

Disease maps are geographical maps that display local estimates of disease risk. When the disease is rare, crude risk estimates can be highly variable, leading to extreme estimates in areas with low population density. Bayesian hierarchical models are commonly used to stabilize the disease map, making them more easily interpretable. By exploiting assumptions about the correlation structure in space and time, the statistical model stabilizes the map by shrinking unstable, extreme risk estimates to the risks in surrounding areas (local spatial smoothing) or to the risks at contiguous time points (temporal smoothing). Extreme estimates that are based on smaller populations are subject to a greater degree of shrinkage, particularly when the risks in adjacent areas or at contiguous time points do not support the extreme value and are more stable themselves. / A common goal in disease mapping studies is to identify areas of elevated risk. The objective of this thesis is to compare the accuracy of several fully Bayesian hierarchical models in discriminating between high-risk and background-risk areas. These models differ according to the various spatial, temporal and space-time interaction terms that are included in the model, which can greatly affect the smoothing of the risk estimates. This was accomplished with simulations based on the cervical cancer rate of Kentucky and at-risk person-years of the state of Kentucky's 120 counties from 1995 to 2002. High-risk areas were 'planted' in the generated maps that otherwise had background relative risks of one. The various disease mapping models were applied and their accuracy in correctly identifying high- and background-risk areas was compared by means of Receiver Operating Characteristic curve methodology. Using data on Multiple Sclerosis (MS) on the island of Sardinia, Italy we apply the more successful models to identify areas of elevated MS risk.

This work presents a technique for shape modelling of cylindrical and spherical tablets subject to compression. This technique is based on the use of partial differential equations (PDEs), the biharmonic equation in particular. The deformation of the compressed elastic-plastic tablet of both shapes was obtained using the existing contact models found in literature. The mathematical properties of the biharmonic equation have been exploited to achieve simple mathematical expressions characterising the shape of the distorted tablet. Thus, the height, radius and contact area of both configurations due to uniaxial compression are represented by analytic expressions relating the coefficients associated with the solution of the biharmonic equation. The results obtained from the PDE-based simulation are compared with the theoretical ones. It is found that the analytic solution of the elliptic PDE can be utilised to represent the physical changes of the deformed object.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 200-214).

Thesis (Ph. D.)--University of Washington, 1989. / Vita. Includes bibliographical references ([217]-228).

Diss. Uppsala : Uppsala universitet, 2007.

Thesis (Ph. D.)--University of Washington, 2008. / Vita. Includes bibliographical references (p. 116-123).

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2008. / Härtill 5 uppsatser.