Inférence statistique dans les processus stochastiques: Aperçu historique

The study of statistical inference for stochastic processes has evolved along two paths. Some problems related to particular processes have been studied, and also some trials to extend general results obtained for independent identically distributed random variables have been made. We retrace the first main contributions, evaluate their influence, and give an idea of the evolution of the research in the field of statistical inference made with observations coming from a stochastic process.     

Summative scales for measuring community satisfaction

Results of this study indicate that pen and paper questionnaire procedures can be successfully adapted to random digit dialing telephone interview methods. Further, the study produced a reliable instrument for measuring attitudes about how residents perceive their communities as a place to live.     

Feasibility and efficacy of a binge drinking prevention intervention for college students delivered via the Internet versus postal mail

In this study, the authors evaluated the feasibility and efficacy of a binge drinking prevention intervention for college students delivered via the Internet versus a parallel print-based intervention delivered via postal mail. A total of 116 college students completed the baseline survey. The authors then randomized the students into the Web or print group and sent them intervention materials. One hundred and six students completed the posttest survey. The intervention consisted of a series of 4 weekly "newsletters" in electronic or print format. The authors collected data using a standardized online 42-item survey. They found that an Internet-based binge drinking prevention intervention for college students was feasible. Results supported the efficacy of this intervention for those students already binge drinking, regardless of delivery mode. The authors found no significant differences on outcome measures when delivered via the Internet or postal mail. Results lend support to the use of the Internet as a viable alternative to more traditional health intervention delivery methods.     

Lessons from Drosophila models of DJ-1 deficiency

Mutations in the DJ-1 gene are associated with rare forms of autosomal recessive early-onset Parkinson's disease (PD). Although the precise physiological function of DJ-1 remains obscure, accumulating evidence suggests that DJ-1 may normally function as a redox-sensitive molecular chaperone that can protect against the deleterious effects of oxidative stress, particularly in mitochondria. Recent studies in the fruit fly, Drosophila melanogaster, have shed further light on the biological role of DJ-1. DJ-1-deficient Drosophila models exhibit distinct phenotypes but collectively highlight a prominent neuroprotective role for DJ-1 against oxidative insult. However, Drosophila lacking DJ-1 do not consistently produce a useful PD-like phenotype (that is, they generally fail to exhibit degeneration of neurons that contain the neurotransmitter dopamine), which may reflect putative compensatory neuroprotective mechanisms. DJ-1-deficient fly models further highlight the utility of Drosophila as an important tool for elucidating protein function and for modeling neurodegenerative disease.     

A Compressive Sensing Based Analysis of Anomalies in Generalized Linear Models

In this article, we present a compressive sensing based framework for generalized linear model regression that employs a two-component noise model and convex optimization techniques to simultaneously detect outliers and determine optimally sparse representations of noisy data from arbitrary sets of basis functions. We then extend our model to include model order reduction capabilities that can uncover inherent sparsity in regression coefficients and achieve simple, superior fits. Second, we use the mixed ?₂/?₁ norm to develop another model that can efficiently uncover block-sparsity in regression coefficients. By performing model order reduction over all independent variables and basis functions, our algorithms successfully deemphasize the effect of independent variables that become uncorrelated with dependent variables. This desirable property has various applications in real-time anomaly detection, such as faulty sensor detection and sensor jamming in wireless sensor networks. After developing our framework and inheriting a stable recovery theorem from compressive sensing theory, we present two simulation studies on sparse or block-sparse problems that demonstrate the superior performance of our algorithms with respect to (1) classic outlier-invariant regression techniques like least absolute value and iteratively reweighted least-squares and (2) classic sparse-regularized regression techniques like LASSO.     

International Adaptation of a Treatment Program for Situational Couple Violence

Intimate Partner Violence (IPV) is a serious international problem. Stith, S. M., McCollum, E. E., and Rosen, K. H. received funding from the US National Institute of Mental Health to develop and test a program for couples experiencing IPV: Domestic Violence Focused Couples Treatment (DVFCT). This article provides an overview of DVFCT, and presents three case studies illustrating how DVFCT has been adapted for use in Colombia, Iran, and Finland. This article emphasizes the need for adapting treatment models to be culturally informed, provides practice-based evidence for DVFCT as a treatment model, and highlights the importance of careful screening and assessment when working with couples who have experienced violence, no matter the country or location where treatment is being conducted.