We present a Markov mixed membership model (Markov M3) for grouped data that learns a fully connected graph structure among mixing components. A key feature of Markov M3 is that it interprets the mixed membership assignment as a Markov random walk over this graph of nodes. This is in contrast to tree-structured models in which the assignment is done according to a tree structure on the mixing c...

The behavior of agricultural commodity markets can arguably result in markedly asymmetric price cycles, that is, downward cycles of substantially different length and breadth than upward cycles. This study assesses whether asymmetric-cycle models can enhance the understanding of the dynamics and provide for a better forecasting of U.S. soybeans and Brazilian coffee prices. The forecasts from as...

Background Drug-induced parkinsonism (DIP) is the most common movement disorder induced by antipsychotics. Although DIP is mostly symmetric, asymmetric DIP is reported in a substantial part of the patients. We investigated the frequency of motor asymmetry in DIP and its relationship to the severity of psychopathology in long-stay psychiatric patients. Methods We obtained data from a cohort st...

In this article, a new class of functional models in which smoothing splines are used to model fixed effects as well as random effects is introduced. The linear mixed effects models are extended to nonparametric mixed effects models by introducing functional random effects, which are modeled as realizations of zero-mean stochastic processes. The fixed functional effects and the random functiona...

We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smo...

Like many other projects, this software library started with a data set and a problem. From this came statistical ideas for a solution, followed by some initial programming — which more than anything else helped to define the real computational and statistical issues — and then a more ambitious programming solution. The problem turned out to be harder than I thought; the first release-worthy co...

Generalized linear models (GLMs) represent a class of fixed effects regression models for several types of dependent variables (i.e., continuous, dichotomous, counts). McCullagh and Nelder [32] describe these in great detail and indicate that the term ‘generalized linear model’ is due to Nelder and Wedderburn [35] who described how a collection of seemingly disparate statistical techniques coul...

Dependent data arise in many studies. For example, children with the same parents or living in neighboring geographic areas tend to be more alike in many characteristics than individuals chosen at random from the population at large; observations taken repeatedly on the same individual are likely to be more similar than observations from different individuals. Frequently adopted sampling design...