ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Filtering by
- All Subjects: Statistics
- Creators: Zheng, Yi
Three topics are investigated in this dissertation with each one focusing on one type of GLMs. Topic I considers GLMs with factorial effects and one continuous covariate. Factors can have interactions among each other and there is no restriction on the possible values of the continuous covariate. The locally D-optimal design structures for such models are identified and results for obtaining smaller optimal designs using orthogonal arrays (OAs) are presented. Topic II considers GLMs with multiple covariates under the assumptions that all but one covariate are bounded within specified intervals and interaction effects among those bounded covariates may also exist. An explicit formula for D-optimal designs is derived and OA-based smaller D-optimal designs for models with one or two two-factor interactions are also constructed. Topic III considers multiple-covariate logistic models. All covariates are nonnegative and there is no interaction among them. Two types of D-optimal design structures are identified and their global D-optimality is proved using the celebrated equivalence theorem.
In the first part of this dissertation, a theoretical result was developed to facilitate the search of locally symmetric optimal designs for mixed responses models with one continuous covariate. Then, the study was extended to mixed responses models that include group effects. Two types of mixed responses models with group effects were investigated. The first type includes models having no common parameters across subject group, and the second type of models allows some common parameters (e.g., a common slope) across groups. In addition to complete class results, an efficient algorithm (PSO-FM) was proposed to search for the A- and D-optimal designs. Finally, the first-order mixed responses model is extended to a type of a quadratic mixed responses model with a quadratic polynomial predictor placed in its linear model.
munity. With life insurance policies and annuity products as dominant financial
instruments which depend on future mortality rates, there is a risk that observed
human mortality experiences will differ from projected when they are sold. From an
insurer’s portfolio perspective, to curb this risk, it is imperative that models of hu
man survivorship are constantly being updated and equipped to accurately gauge and
forecast mortality rates. At present, the majority of actuarial research in mortality
modeling involves factor-based approaches which operate at a global scale, placing
little attention on the determinants and interpretable risk factors of mortality, specif
ically from a spatial perspective. With an abundance of research being performed
in the field of spatial statistics and greater accessibility to localized mortality data,
there is a clear opportunity to extend the existing body of mortality literature to
wards the spatial domain. It is the objective of this dissertation to introduce these
new statistical approaches to equip the field of actuarial science to include geographic
space into the mortality modeling context.
First, this dissertation evaluates the underlying spatial patterns of mortality across
the United States, and introduces a spatial filtering methodology to generate latent
spatial patterns which capture the essence of these mortality rates in space. Second,
local modeling techniques are illustrated, and a multiscale geographically weighted
regression (MGWR) model is generated to describe the variation of mortality rates
across space in an interpretable manner which allows for the investigation of the
presence of spatial variability in the determinants of mortality. Third, techniques for
updating traditional mortality models are introduced, culminating in the development
of a model which addresses the relationship between space, economic growth, and
mortality. It is through these applications that this dissertation demonstrates the
utility in updating actuarial mortality models from a spatial perspective.
In the first paper, an alternative approach to the partitioned Generalized Method of Moments logistic regression model for longitudinal binary outcomes is presented. This method relies on Bayes estimators and is utilized when the partitioned Generalized Method of Moments model provides numerically unstable estimates of the regression coefficients. It is used to model obesity status in the Add Health study and cognitive impairment diagnosis in the National Alzheimer’s Coordination Center database.
The second paper develops a model that allows the joint modeling of two or more binary outcomes that provide an overall measure of a subject’s trait over time. The simultaneous modelling of all outcomes provides a complete picture of the overall measure of interest. This approach accounts for the correlation among and between the outcomes across time and the changing effects of time-dependent covariates on the outcomes. The model is used to analyze four outcomes measuring overall the quality of life in the Chinese Longitudinal Healthy Longevity Study.
The third paper presents an approach that allows for estimation of cross-sectional and lagged effects of the covariates on the outcome as well as the feedback of the response on future covariates. This is done in two-parts, in part-1, the effects of time-dependent covariates on the outcomes are estimated, then, in part-2, the outcome influences on future values of the covariates are measured. These model parameters are obtained through a Generalized Method of Moments procedure that uses valid moment conditions between the outcome and the covariates. Child morbidity in the Philippines and obesity status in the Add Health data are analyzed.