Abstract
Nonparametric regression models are proposed in the framework of ecological inference for exploratory modeling of disease prevalence rates adjusted for variables, such as age, ethnicity/race, and socio-economic status. Ecological inference is needed when a response variable and covariate are not available at the subject level because only summary statistics are available for the reporting unit, for example, in the form of R x C tables. In this article, only the marginal counts are assumed available in the sample of R x C contingency tables for modeling the joint distribution of counts. A general form for the ecological regression model is proposed, whereby certain covariates are included as a varying coefficient regression model, whereas others are included as a functional linear model. The nonparametric regression curves are modeled as splines fit by penalized weighted least squares. A data-driven selection of the smoothing parameter is proposed using the pointwise maximum squared bias computed from averaging kernels (explained by O'Sullivan, 1986, Statistical Science 1, 502-517). Analytic expressions for bias and variance are provided that could be used to study the rates of convergence of the estimators. Instead, this article focuses on demonstrating the utility of the estimators in a study of disparity in health outcomes by ethnicity/race.