Importance sampling with the generalized exponential power density

In this paper, the generalized exponential power (GEP) density is proposed as an importance function in Monte Carlo simulations in the context of estimation of posterior moments of a location parameter. This density is divided in five classes according to its tail behaviour which may be exponential, polynomial or logarithmic. The notion of p-credence is also defined to characterize and to order the tails of a large class of symmetric densities by comparing their tails to those of the GEP density.The choice of the GEP density as an importance function allows us to obtain reliable and effective results when p-credences of the prior and the likelihood are defined, even if there are conflicting sources of information. Characterization of the posterior tails using p-credence can be done. Hence, it is possible to choose parameters of the GEP density in order to have an importance function with slightly heavier tails than the posterior. Simulation of observations from the GEP density is also addressed.     

Bayesian optimal design for changepoint problems

We investigate Bayesian optimal designs for changepoint problems. We find robust optimal designs which allow for arbitrary distributions before and after the change, arbitrary prior densities on the parameters before and after the change, and any log‐concave prior density on the changepoint. We define a new design measure for Bayesian optimal design problems as a means of finding the optimal design. Our results apply to any design criterion function concave in the design measure. We illustrate our results by finding the optimal design in a problem motivated by a previous clinical trial. The Canadian Journal of Statistics 37: 495–513; 2009 © 2009 Statistical Society of Canada     

A test for crossing receiver operating characteristic (roc) curves

The receiver operating characteristic (ROC) curve gives a graphical representation of sensitivity and specificity of a prediction model when varying the decision treshold on a diagnostic criterion. A classical test for comparing the overall accuracies for two models -1 and 2- is based on the difference between ROC curves areas - related to its standard error. This test is designed for the situation where ROC curve 1 caps ROC curve 2. Often both curves cross :in this paper, a new test, based on the integrated difference between the curves, is proposed to deal with this situation. In a simulation experiment, the new test was less powerful than the old test for detecting an overall superiority, but much more powerfull against the crossing alternative.     

Distribution asymptotique des autocorrélations ďun processus saisonnier non stationnaire

We examine the behaviour of the sample autocorrelations of a seasonal time series for which the first difference of order s (s ≥ 1) is stationary. The asymptotic distribution of the autocorrelations r'(k) based on uncentered data and of the autocorrelations r(k) based on centered data are derived. In each case, the asymptotic distribution is characterized as a function of the lag k and the parameters of the process. A simulation study was conducted in order to investigate the rate of convergence of the finite sample distributions of r(k) and r'(k) to their asymptotic counterparts and to evaluate the effect of centering or not centering the data on the distribution of autocorrelations.     

Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis

Model-based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model-based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM); (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony-based approach and an evaluation graph-based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.     

Group decision making under uncertainty a note on the aggregation of “ordinal probabilities”

This paper is a first attempt to study the problem of aggregation of individual ordinal probabilistic beliefs in an Arrowian framework. We exhibit some properties an aggregation rule must fulfil; in particular we prove the existence of a quasi-dictator.     

Exploring team mental model dynamics during strategic change implementation in professional service organizations. A sensemaking perspective

The purpose of this paper is to provide a deeper process understanding of team mental model dynamics in a context of strategic change implementation. To do so, we adopt a change recipient sensemaking perspective with the objective to identify salient determinants of team mental model dynamics. We aim to contribute to the managerial and organizational cognition literature by identifying critical micro-foundations that shape team cognition and interpretation processes during strategic change implementation. This adds to the field’s understanding of the under-researched collective dimension of strategic processes in general and strategic change implementation more specifically. Through an explorative case study conducted at a professional service organization, we identified five determinants of team mental model dynamics: coherence between ostensive and performative aspects of organizational routines, equivocality of expectations, dominance of organizational discourse, shifts in organizational identification and cross-understanding between departmental thought worlds. Case findings reveal that implementation processes of strategic change become intricate and difficult if change recipient sensemaking is not effectively acted upon. The five determinants identified require adequate managerial attention in order to avoid slipping into organizational inertia. As a consequence, professional workers are unable to ‘drop their tools’ and fail to integrate the strategic change effort in updated team mental models.     

The Future Composition of the Canadian Labor Force: A Microsimulation Projection

This article charts the future transformations of the Canadian labor force population using a microsimulation projection model. The model takes into account differentials in demographic behavior and labor force participation of individuals according to their ethnocultural and educational characteristics. As a result of a rapid fall in fertility, the Canadian population is expected to age rapidly as baby boomers start to retire from the labor market in large numbers. In response to declining fertility, Canada raised its immigration intake at the end of the 1980s, and immigration is now the main driver of Canadian population growth. At the same time, immigrants to Canada are becoming more culturally diversified. Over the last half century, the main source regions have shifted from Europe to Asia. Results of the microsimulation show that Canada's labor force population will continue to increase, but at a slower rate than in the recent past. By 2031, almost one third of the country's total labor force could be foreign‐born, and almost all its future increase is expected to be among university graduates, while the less‐educated labor force is projected to decline.