In this paper, a comparison of weak-form Galerkin and least-squares finite element models of Timoshenko beam theory with the von Kármán strains is presented. Computational characteristics of the two models and the influence of the polynomial orders used on the relative accuracies of the two models are discussed. The degree of approximation functions used varied from linear to the 5th order. In ...

with the growth in customers’ demand diversification, mixed-model u-lines (mmul) have acquired increasing importance in the area of assembly systems. there are generally two different approaches in the literature for balancing such systems. some researchers believe that since the types of models can be very diverse, a balancing approach without simultaneously sequencing of models will not yield...

In linear mixed models the influence of covariates is restricted to a strictly parametric form. With the rise of semiand nonparametric regression also the mixed model has been expanded to allow for additive predictors. The common approach uses the representation of additive models as mixed models. An alternative approach that is proposed in the present paper is likelihood based boosting. Boosti...

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...

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...