Statistical methodology is presented for the regression analysis of multiple events in the presence of random eeects and measurement error. Omitted covariates are modeled as random eeects. Our approach to parameter estimation and signiicance testing is to start with a naive model of semi-parametric Poisson process regression, and then to adjust for random eeects and any possible covariate measu...

Spatial data with covariate measurement errors have been commonly observed in public health studies. Existing work mainly concentrates on parameter estimation using Gibbs sampling, and no work has been conducted to understand and quantify the theoretical impact of ignoring measurement error on spatial data analysis in the form of the asymptotic biases in regression coefficients and variance com...

SUMMARY Nonparametric Bayesian methods have proven to be extremely useful for providing flexible models that are capable of fitting an extraordinarily wide array of data sets. Two of their most natural uses are in providing distributions for random effects and in providing large classes of models that elaborate on a para-metric model. These models are appropriate for a great many data sets, all...

BACKGROUND Spatial epidemiology has been aided by advances in geographic information systems, remote sensing, global positioning systems and the development of new statistical methodologies specifically designed for such data. Given the growing popularity of these studies, we sought to review and analyze the types of spatial measurement errors commonly encountered during spatial epidemiological...

We study joint modeling of survival and longitudinal data. There are two regression models of interest. The primary model is for survival outcomes, which are assumed to follow a time-varying coefficient proportional hazards model. The second model is for longitudinal data, which are assumed to follow a random effects model. Based on the trajectory of a subject's longitudinal data, some covariat...

The ordinary maximum likelihood (ML) approach in classical regression models, fails when the independent variables are subject to error. The most noticeable and well known problem reported in the literature is the inconsistency of the ML estimators [1]. To solve this problem, a number of alternatives were proposed. The measurement error model (MEM) is the most fashionable of them, but it has so...

‘Distribution regression’ refers to the situation where a response Y depends on a covariate P where P is a probability distribution. The model is Y = f(P ) + μ where f is an unknown regression function and μ is a random error. Typically, we do not observe P directly, but rather, we observe a sample from P . In this paper we develop theory and methods for distribution-free versions of distributi...