The existence of disparities among communities of diverse racial and economic backgrounds has received considerable attention in last two decades or so. Scholars, bureaucrats, legislators, governing bodies, and numerous other constituents share a sustained concern about the persistent trend of disparity across diverse communities. Today, there is an increasing demand for quantitative tools to analyze such disparity information from high-throughput sources. Simple descriptive statistics that relate to socio-economic, socio-demographic, and socio-health deprivation measures often can mask the important features while analyzing such data. Thus, the exploration of disparity can be better understood by developing sophisticated statistical tools that can extract the salient features from complex data sources. This project will explore innovative multilevel modeling techniques to develop measures that are scientifically more efficient and meaningful for these purposes. Specifically, the research will develop new small area estimation models and estimation techniques that encompass mean-variance relationship, distributional robustness, and multiple comparisons using hierarchical and nonparametric Bayesian approaches. Furthermore, some estimating equation approaches will be developed to study the association between socio-demographic and socio-economic variables with cancer incidence, linked via multilevel generalized linear models. The methods potentially are suitable for analyzing high-dimensional and sparse data.
The statistical development will enrich small area estimation technique in various dimensions. The Dirichlet process-based robust modeling will advance the research on clustering in the context of analyzing socio-economic data. The multiple comparison procedures will enhance the simultaneous inference literature in the context of hierarchical modeling. The methods also will have implications in other areas of research such as education, epidemiology, and genetics. In addition, the project will contribute towards research-based training of graduate students and foster interdisciplinary collaboration.
