In this work we give a complete description of the set covering polyhedron of circulant matrices $$C^k_{sk}$$ C s k k with $$s=2,3$$ s = 2 , 3 and $$k \ge 3 $$ k ≥ 3 by linear inequalities. In particular, we prove that every non boolean facet defining inequality is associated with a circulant...

We computationally assess policies for the elevator control problem by a new column-generation approach for the linear programming method for discounted infinite-horizon Markov decision problems. By analyzing the optimality of given actions in given states, we were able to provably improve the...

In some multivariate problems with missing data, pairs of variables exist that are never observed together. For example, some modern biological tools can produce data of this form. As a result of this structure, the covariance matrix is only partially identifiable, and point estimation requires...

A Bayesian model is proposed for estimating abundance or density of animals from passive acoustic binary data. The data are collected at points along one or more transects, and the points are spaced so that a single individual can be heard multiple times. Thus successive data points are...

Nonlinear mixed effect models (NLMEM) are used in model-based drug development to analyse longitudinal data. To design these studies, the use of the expected Fisher information matrix (MF) is a good alternative to clinical trial simulation. Presently, MF in NLMEM is mostly evaluated with...

Random effects in generalized linear mixed models (GLMM) are used to explain the serial correlation of the longitudinal categorical data. Because the covariance matrix is high dimensional and should be positive definite, its structure is assumed to be constant over subjects and to be restricted...

An efficient, generic and simple to use Markov chain Monte Carlo (MCMC) algorithm for partially observed temporal epidemic models is introduced. The algorithm is designed to be adaptive so that it can easily be used by non-experts. There are two key features incorporated in the algorithm to...

This article develops a mean field variational Bayes approximation algorithm for posterior inferences of the recently proposed partially linear additive models with simultaneous and automatic variable selection and linear/nonlinear component identification abilities. To solve the problem induced...

A system to update estimates from a sequence of probability distributions is presented. The aim of the system is to quickly produce estimates with a user-specified bound on the Monte Carlo error. The estimates are based upon weighted samples stored in a database. The stored samples are...

The partially linear single-index model is an interesting semiparametric model extended by the partially linear model and the single-index model, which supply a good balance between flexibility and parsimony. A robust estimation is proposed to fit the partially linear single-index model in case...