Model-based clustering of Gaussian copulas for mixed data

Clustering of mixed data is important yet challenging due to a shortage of conventional distributions for such data. In this article, we propose a mixture model of Gaussian copulas for clustering mixed data. Indeed copulas, and Gaussian copulas in particular, are powerful tools for easily modeling the distribution of multivariate variables. This model clusters data sets with continuous, integer, and ordinal variables (all having a cumulative distribution function) by considering the intra-component dependencies in a similar way to the Gaussian mixture. Indeed, each component of the Gaussian copula mixture produces a correlation coefficient for each pair of variables and its univariate margins follow standard distributions (Gaussian, Poisson, and ordered multinomial) depending on the nature of the variable (continuous, integer, or ordinal). As an interesting by-product, this model generalizes many well-known approaches and provides tools for visualization based on its parameters. The Bayesian inference is achieved with a Metropolis-within-Gibbs sampler. The numerical experiments, on simulated and real data, illustrate the benefits of the proposed model: flexible and meaningful parameterization combined with visualization features.     

Variable selection for model-based clustering using the integrated complete-data likelihood

Variable selection in cluster analysis is important yet challenging. It can be achieved by regularization methods, which realize a trade-off between the clustering accuracy and the number of selected variables by using a lasso-type penalty. However, the calibration of the penalty term can suffer from criticisms. Model selection methods are an efficient alternative, yet they require a difficult optimization of an information criterion which involves combinatorial problems. First, most of these optimization algorithms are based on a suboptimal procedure (e.g. stepwise method). Second, the algorithms are often computationally expensive because they need multiple calls of EM algorithms. Here we propose to use a new information criterion based on the integrated complete-data likelihood. It does not require the maximum likelihood estimate and its maximization appears to be simple and computationally efficient. The original contribution of our approach is to perform the model selection without requiring any parameter estimation. Then, parameter inference is needed only for the unique selected model. This approach is used for the variable selection of a Gaussian mixture model with conditional independence assumed. The numerical experiments on simulated and benchmark datasets show that the proposed method often outperforms two classical approaches for variable selection. The proposed approach is implemented in the R package VarSelLCM available on CRAN.     

Concentration inequalities of the cross-validation estimator for empirical risk minimizer

ABSTRACT

We derive concentration inequalities for the cross-validation estimate of the generalization error for empirical risk minimizers. In the general setting, we show that the worst-case error of this estimate is not much worse that of training error estimate see Kearns M, Ron D. [Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural Comput. 1999;11:1427–1453]. General loss functions and class of predictors with finite VC-dimension are considered. Our focus is on proving the consistency of the various cross-validation procedures. We point out the interest of each cross-validation procedure in terms of rates of convergence. An interesting consequence is that the size of the test sample is not required to grow to infinity for the consistency of the cross-validation procedure.     

Latin hypercube sampling with inequality constraints

In some studies requiring predictive and CPU-time consuming numerical models, the sampling design of the model input variables has to be chosen with caution. For this purpose, Latin hypercube sampling has a long history and has shown its robustness capabilities. In this paper we propose and discuss a new algorithm to build a Latin hypercube sample (LHS) taking into account inequality constraints between the sampled variables. This technique, called constrained Latin hypercube sampling (cLHS), consists in doing permutations on an initial LHS to honor the desired monotonic constraints. The relevance of this approach is shown on a real example concerning the numerical welding simulation, where the inequality constraints are caused by the physical decreasing of some material properties in function of the temperature.     

Perceived family impact of volunteering among reintegrating post-9/11 Veterans

Family issues are common among returned post-9/11 Veterans. Traumatic brain injury (TBI), post-traumatic stress disorder (PTSD), and depression are each independently related to divorce whereas community ties and social support are protective factors for the family during reintegration. Evidence from elders on the benefits of one intervention, community volunteering, may indicate “spillover effects” of these benefits into the family. Few measures exist to assess the impact of military Veteran volunteering on the family. The authors report (1) an adaption of a benefits measure from elders to Veterans, (2) its preliminary reliability and validity, and (3) differences among subgroups. Reintegrating post-9/11 Veterans (N = 346) who completed a 6-month, stipended volunteer program were surveyed. Perceived impact of volunteering on the family was assessed after completion of the program using an 11-item self-report measure. Rank-based nonparametric tests were used to detect significant differences among subgroups. Preliminary findings support the scale’s adaptation to Veterans, internal consistency, and construct validity. At least one perceived family impact indicator differed significantly (p < .05) between subgroups based on demographic and psychological factors. Veterans in this civic service program perceived that their volunteering may have affected their families.     

Les ajustements de la technique médicale face aux règles de droit

During the 1990s, partly as a result of accusations of medical malpractice, doctors’ obligations were reinforced; and patients’ rights, recognized. In a lawsuit brought on the grounds of liability, the doctor and his/her qualifications are accused; and the doctor's identity and sense of professional honor, affected. This has serious risks for medical practices and, more broadly, medicine. This research based on interviews with doctors shows that the specialty, type of practice and nature of the establishment expose doctors to more or fewer risks and thus partly determine how they adjust techniques to cope with the risks of a lawsuit. The professional identity (including the values and practices to which interviewees referred) and the consciousness of their symbolic status (related to the place and type of practice) are keys for understanding practitioners’ behaviors. Assuming that a doctor's sense of professional identity is shaped by his/her sense of responsibility, relations with patients and concern for achievement, it is hypothesized that these three dimensions and their possible combinations explain the adjustments, or lack thereof, made by doctors.