The effect of variability and carryover on average bioequivalence assessment: a simulation study

The purpose of this study was to evaluate the effect of residual variability and carryover on average bioequivalence (ABE) studies performed under a 22 crossover design. ABE is usually assessed by means of the confidence interval inclusion principle. Here, the interval under consideration was the standard 'shortest' interval, which is the mainstream approach in practice. The evaluation was performed by means of a simulation study under different combinations of carryover and residual variability besides of formulation effect and sample size. The evaluation was made in terms of percentage of ABE declaration, coverage and interval precision. As is well known, high levels of variability distort the ABE procedures, particularly its type II error control (i.e. high variabilities make difficult to declare bioequivalence when it holds). The effect of carryover is modulated by variability and is especially disturbing for the type I error control. In the presence of carryover, the risk of erroneously declaring bioequivalence may become high, especially for low variabilities and large sample sizes. We end up with some hints concerning the controversy about pretesting for carryover before performing ABE analysis.     

On convergence of the EM algorithmand the Gibbs sampler

In this article we investigate the relationship between the EM algorithm and the Gibbs sampler. We show that the approximate rate of convergence of the Gibbs sampler by Gaussian approximation is equal to that of the corresponding EM-type algorithm. This helps in implementing either of the algorithms as improvement strategies for one algorithm can be directly transported to the other. In particular, by running the EM algorithm we know approximately how many iterations are needed for convergence of the Gibbs sampler. We also obtain a result that under certain conditions, the EM algorithm used for finding the maximum likelihood estimates can be slower to converge than the corresponding Gibbs sampler for Bayesian inference. We illustrate our results in a number of realistic examples all based on the generalized linear mixed models.     

Residuals for log-Burr XII regression models in survival analysis

In this paper, we compare three residuals to assess departures from the error assumptions as well as to detect outlying observations in log-Burr XII regression models with censored observations. These residuals can also be used for the log-logistic regression model, which is a special case of the log-Burr XII regression model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the modified martingale-type residual in log-Burr XII regression models with censored data.     

Likelihood‐based inference for correlated diffusions

The authors address the problem of likelihood‐based inference for correlated diffusions. Such a task presents two issues; the positive definite constraints of the diffusion matrix and the likelihood intractability. The first issue is handled by using the Cholesky factorization on the diffusion matrix. To deal with the likelihood unavailability, a generalization of the data augmentation framework of Roberts and Stramer [Roberts and Stramer (2001) Biometrika 88(3), 603–621] to d‐dimensional correlated diffusions, including multivariate stochastic volatility models, is given. The methodology is illustrated through simulated and real data sets. The Canadian Journal of Statistics 39: 52–72; 2011 © 2011 Statistical Society of Canada     

Performance analysis of a multi-agent scheduling and control system under manufacturing disturbances

This paper is concerned with evaluating the performance of alternative multi-agent approaches to manufacturing planning and control. In order to separate the influence of ‘control algorithm’ from that of ‘control architecture’, a simple local control algorithm is chosen as a common starting point for developing the agent-based system. Two sets of experiments are then reported to evaluate how changes in individual agent characteristics can affect the control system's flexibility and adaptability against disturbances. These experiments show that the manufacturing and control system performance is not affected by architecture if the control algorithm remains fixed, however, altering the characteristics of the control system decision-makers influences both systems’ performance.     

Action-research and the Production of Knowledge in a Teacher Education based on Inter/Multicultural Education

This paper proposes that the teacher, on the basis of action-research methodology, can produce two types of scientific knowledge: one based on the teacher as researcher (the teacher as ethnographer), and another based on the development of pedagogic action (the teacher as educator). The education of such a teacher, simultaneously a researcher and an educator, is carried out through the concretisation of that which has denominated the interface of intercultural education. The development of this interface renders possible the management of diversity by the teacher. This diversity is present both in the school, as well as, more specifically, in the classroom. It can also be seen as a source of wealth for the further deepening of the democratic nature of both the school and the education system.     

Perfect slice samplers

Perfect sampling allows the exact simulation of random variables from the stationary measure of a Markov chain. By exploiting monotonicity properties of the slice sampler we show that a perfect version of the algorithm can be easily implemented, at least when the target distribution is bounded. Various extensions, including perfect product slice samplers, and examples of applications are discussed.