Forecasting with non-homogeneous hidden Markov models

We present a Bayesian forecasting methodology of discrete-time finite state-space hidden Markov models with non-constant transition matrix that depends on a set of exogenous covariates. We describe an MCMC reversible jump algorithm for predictive inference, allowing for model uncertainty regarding the set of covariates that affect the transition matrix. We apply our models to interest rates and we show that our general model formulation improves the predictive ability of standard homogeneous hidden Markov models.     

An Examination of the Robustness to Non Normality of the EWMA Control Charts for the Dispersion

ABSTRACT

The EWMA control chart is used to detect small shifts in a process. It has been shown that, for certain values of the smoothing parameter, the EWMA chart for the mean is robust to non normality. In this article, we examine the case of non normality in the EWMA charts for the dispersion. It is shown that we can have an EWMA chart for dispersion robust to non normality when non normality is not extreme.     

The use of Andrews curves for detecting the out-of-control variables when a multivariate control chart signals

The aim of this paper is to present a new method for solving the problem of detecting the out-of-control variables when a multivariate control chart signals. The main idea is based on Andrews curves. The proposed method is investigated thoroughly and is proved to have interesting results in comparison to a competing method.     

An alternative parameterization of Bayesian logistic hierarchical models for mixed treatment comparisons

Mixed treatment comparison (MTC) models rely on estimates of relative effectiveness from randomized clinical trials so as to respect randomization across treatment arms. This approach could potentially be simplified by an alternative parameterization of the way effectiveness is modeled. We introduce a treatment‐based parameterization of the MTC model that estimates outcomes on both the study and treatment levels. We compare the proposed model to the commonly used MTC models using a simulation study as well as three randomized clinical trial datasets from published systematic reviews comparing (i) treatments on bleeding after cirrhosis, (ii) the impact of antihypertensive drugs in diabetes mellitus, and (iii) smoking cessation strategies. The simulation results suggest similar or sometimes better performance of the treatment‐based MTC model. Moreover, from the real data analyses, little differences were observed on the inference extracted from both models. Overall, our proposed MTC approach performed as good, or better, than the commonly applied indirect and MTC models and is simpler, fast, and easier to implement in standard statistical software. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessment of Athens's metro passenger behaviour via a multiranked probit model

Summary. We deal with real data from a stated preference experiment which was designed to explain and predict passengers' behaviour towards three main means of transportation in the city of Athens. The resulting model formulations give rise to the so-called multiranked probit model which emerges from a series of ranking responses in a set of hypothetical scenarios, i.e. we enhance the multinomial probit model with the embodiment of a utility threshold parameter which deals realistically with ranking responses, intransitivity of indifference between alternatives or ties. Moreover, we ensure identifiable parameters for the covariance matrix of the underlying utility vectors, we include a hierarchical step that models the unit-specific utility thresholds as exchangeably distributed and, finally, we permit the use of heavy-tailed distributions for the stochastic error term. Our proposed methodology is Bayesian and the implementation tool adopted is Markov chain Monte Carlo sampling. The posterior output consists of practical information such as travel characteristics (e.g. walking times and waiting times), expressed either in drachmas per hour or in minutes of in-vehicle time, and 95% credible intervals of the probability of choosing a particular mode of transportation. These are key factors in determining whether a policy has positive or negative net benefits.