Cardiometabolic diseases have substantially increased in China in the past 20 years and blood pressure is a primary modifiable risk factor. Using data from the China Health and Nutrition Survey we examine blood pressure trends in China from 1991 to 2009, with a concentration on age cohorts and urbanicity. Very large values of blood pressure are of interest, so we model the conditional quantile ...

We propose a fully Bayesian methodology for generalized kernel mixed models (GKMMs), which are extensions of generalized linear mixed models in the feature space induced by a reproducing kernel. We place a mixture of a point-mass distribution and Silverman’s g-prior on the regression vector of a generalized kernel model (GKM). This mixture prior allows a fraction of the components of the regres...

Stochastic differential equations have been shown useful in describing random continuous time processes. Biomedical experiments often imply repeated measurements on a series of experimental units and differences between units can be represented by incorporating random effects into the model. When both system noise and random effects are considered, stochastic differential mixed-effects models e...

The seminal papers by Nelder and Wedderburn (Generalized Linear Models, JRSS A 1972) and Cox (Regression models and life tables, JRSS B 1972) both rely on the assumption that conditionally on covariate information (including time) the observations are independent. The difficulty in identifying and measuring all relevant covariates has pushed for methods that can handle both mean and covariance ...

The R statistic, when used in a regression or ANOVA context, is appealing because it summarizes how well the model explains the data in an easy-tounderstand way. R statistics are also useful to gauge the effect of changing a model. Generalizing R to mixed models is not obvious when there are correlated errors, as might occur if data are georeferenced or result from a designed experiment with bl...

Increasingly, scientific studies yield functional data, in which the ideal units of observation are curves and the observed data consist of sets of curves that are sampled on a fine grid. We present new methodology that generalizes the linear mixed model to the functional mixed model framework, with model fitting done by using a Bayesian wavelet-based approach. This method is flexible, allowing...

We present here Mixed Mode Cascaded Classification Models, an algorithmic framework that seeks to effectively solve a wide range of machine learning tasks in a “plug and play” manner. It does this by sharing predictions between machine learning tasks, thus giving each task additional high-level information that can be used to solve its specific problem. We consider here a specific implementatio...