There has been significant recent interest in dictionary learning and sparse coding, with applications in denoising, interpolation, feature extraction, and classification [1]–[3]. Increasingly it has been recognized that these models may be improved by imposing additional prior information, beyond sparseness. For example, a locality constraint has been used successfully in the context of featur...

We develop a new approach to using estimating equations to estimate marginal regression models for longitudinal data with time-dependent covariates. Our approach classifies time-dependent covariates into three types – Types I, II and III. The type of covariate determines what estimating equations can be used involving the covariate. We use the generalised method of moments to make optimal use o...

Multi-type recurrent event data occur frequently in longitudinal studies. Dependent termination may occur when the terminal time is correlated to recurrent event times. In this article, we simultaneously model the multi-type recurrent events and a dependent terminal event, both with nonparametric covariate functions modeled by B-splines. We develop a Bayesian multivariate frailty model to accou...

The impact of covariate aggregation, well studied in relation to linear regression, is less clear in the Cox model. In this paper, the authors use real-life epidemiologic data to illustrate how aggregating individual covariate values may lead to important underestimation of the exposure effect. The issue is then systematically assessed through simulations, with six alternative covariate represe...

Covariate-adjusted regression was recently proposed for situations where both predictors and response in a regression model are not directly observed, but are observed after being contaminated by unknown functions of a common observable covariate. The method has been appealing because of its flexibility in targeting the regression coefficients under different forms of distortion. We extend this...

Real-life count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. The result of...

Cancers of the breast and endometrium contribute substantially to mortality among women. Studies examining putative links between these cancers and estrogen have reported connicting results. The model presented here provides a natural framework for exploring the eeect of dependent covariate subclasses in explaining the variation in the response. Such subclass eeects can be investigated across s...