Tests statistiques et IRM cérébrales en classe de première S

International audienceLes images IRM fonctionnelles (IRMf) ont permis une avancée importante dans la compréhension du fonctionnement cérébral. Loin d’être de simples « photographies » du cerveau en fonctionnement, ces images résultent de protocoles de construction complexes et ont une signification statistique. La compréhension de leur nature statistique est difficile, mais nécessaire pour appréhender leur domaine de validité et construire une attitude critique face à leur utilisation, notamment dans les médias. Les représentations de ces images construites par les élèves ne prennent généralement pas en compte cette dimension statistique. Nous présentons et analysons une séquence d’apprentissage innovante mise en œuvre pour favoriser la compréhension de ce type d’image. Il s’agit d’une séance de modélisation comprenant le paramétrage d’IRMf à l’aide du logiciel EduAnatomist, précédée d’une activité d’appropriation de la notion de test statistique (test T). Nos résultats font apparaitre les difficultés inhérentes à la prise en compte par les élèves de l’aspect statistique des images biologiques. Nous proposons des pistes didactiques pour ouvrir, au moins en partie, certaines des boites noires qui correspondent à la construction de ce type d’image

Defining a robust biological prior from Pathway Analysis to drive Network Inference

Inferring genetic networks from gene expression data is one of the most challenging work in the post-genomic era, partly due to the vast space of possible networks and the relatively small amount of data available. In this field, Gaussian Graphical Model (GGM) provides a convenient framework for the discovery of biological networks. In this paper, we propose an original approach for inferring gene regulation networks using a robust biological prior on their structure in order to limit the set of candidate networks. Pathways, that represent biological knowledge on the regulatory networks, will be used as an informative prior knowledge to drive Network Inference. This approach is based on the selection of a relevant set of genes, called the "molecular signature", associated with a condition of interest (for instance, the genes involved in disease development). In this context, differential expression analysis is a well established strategy. However outcome signatures are often not consistent and show little overlap between studies. Thus, we will dedicate the first part of our work to the improvement of the standard process of biomarker identification to guarantee the robustness and reproducibility of the molecular signature. Our approach enables to compare the networks inferred between two conditions of interest (for instance case and control networks) and help along the biological interpretation of results. Thus it allows to identify differential regulations that occur in these conditions. We illustrate the proposed approach by applying our method to a study of breast cancer's response to treatment

Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models

Mixture of Experts (MoE) are successful models for modeling heterogeneous data in many statistical learning problems including regression, clustering and classification. Generally fitted by maximum likelihood estimation via the well-known EM algorithm, their application to high-dimensional problems is still therefore challenging. We consider the problem of fitting and feature selection in MoE models, and propose a regularized maximum likelihood estimation approach that encourages sparse solutions for heterogeneous regression data models with potentially high-dimensional predictors. Unlike state-of-the art regularized MLE for MoE, the proposed modelings do not require an approximate of the penalty function. We develop two hybrid EM algorithms: an Expectation-Majorization-Maximization (EM/MM) algorithm, and an EM algorithm with coordinate ascent algorithm. The proposed algorithms allow to automatically obtaining sparse solutions without thresholding, and avoid matrix inversion by allowing univariate parameter updates. An experimental study shows the good performance of the algorithms in terms of recovering the actual sparse solutions, parameter estimation, and clustering of heterogeneous regression data

Analysing occupational safety culture through mass media monitoring

In the last years, a group of researchers within the National Institute for Insurance against Accidents at Work (INAIL) has launched a pilot project about mass media monitoring in order to find out how the press deal with the culture of safety and health at work. To monitor mass media, the Institute has created a relational database of news concerning occupational injuries and diseases, that was filled with information obtained from the newspaper articles about work-related accidents and incidents, including the text itself of the articles. In keeping with that, the ultimate objective is to identify the major lines for awareness-raising actions on safety and health at work. In a first phase of this project, 1,858 news articles regarding 580 different accidents were collected; for each injury, not only the news texts but also several variables were identified. Our hypothesis is that, for different kind of accidents, a different language is used by journalists to narrate the events. To verify it, a text clustering procedure is implemented on the articles, together with a Lexical Correspondence Analysis; our purpose is to find language distinctions connected to groups of similar injuries. The identification of various ways in reporting the events, in fact, could provide new elements to describe safety knowledge, also establishing collaborations with journalists in order to enhance the communication and raise people attention toward workers' safety

Multi-mode partitioning for text clustering to reduce dimensionality and noises

Co-clustering in text mining has been proposed to partition words and documents simultaneously. Although the main advantage of this approach may improve interpretation of clusters on the data, there are still few proposals on these methods; while one-way partition is even now widely utilized for information retrieval. In contrast to structured information, textual data suffer of high dimensionality and sparse matrices, so it is strictly necessary to pre-process texts for applying clustering techniques. In this paper, we propose a new procedure to reduce high dimensionality of corpora and to remove the noises from the unstructured data. We test two different processes to treat data applying two co-clustering algorithms; based on the results we present the procedure that provides the best interpretation of the data

Fast change point analysis on the Hurst index of piecewise fractional Brownian motion

In this presentation, we introduce a new method for change point analysis on the Hurst index for a piecewise fractional Brownian motion. We first set the model and the statistical problem. The proposed method is a transposition of the FDpV (Filtered Derivative with p-value) method introduced for the detection of change points on the mean in Bertrand et al. (2011) to the case of changes on the Hurst index. The underlying statistics of the FDpV technology is a new statistic estimator for Hurst index, so-called Increment Bernoulli Statistic (IBS). Both FDpV and IBS are methods with linear time and memory complexity, with respect to the size of the series. Thus the resulting method for change point analysis on Hurst index reaches also a linear complexity

Détection statistique d'une anomalie à partir de projections tomographiques

- La détection d'une anomalie à partir de quelques projections tomographiques bruitées est considérée d'un point de vue statistique. La scène bidimensionnelle étudiée est composée d'un environnement inconnu, considéré comme un paramètre de nuisance, et d'une éventuelle anomalie. Un test invariant optimal est alors proposé pour détecter l'anomalie

The shape of the relationship between mortality and income in France.

Cette recherche explore la forme de relation existante entre probabilité de décès et revenu en France, sur la base d’une étude cas-témoins constituée à partir de deux bases de données fiscales. Les résultats montrent que le risque de décès est fortement corrélé au niveau de revenu, après contrôle par la profession. Cette relation existe tout au long de la distribution des revenus. En particulier , l’effet protecteur des plus hauts revenus remet en cause l’hypothèse de concavité de la relation revenu-santé.Using a case-control study constructed with two fiscal databases, this paper investigates the shape of the relationship between income and the probability of death in France. The results show that the risk of mortality is strongly correlated with the level of income, independent from the occupational status. This relationship holds across the whole range of income distribution. Specifically the protective effect of highest incomes casts some doubt on the hypothesis of the concavity of the income-health relationship.Mortality; Health inequalities; Income;

Readiness for Hospital Discharge Scale for older people: psychometric testing and short form development with a three country sample

Aim To develop and psychometrically test Readiness for Hospital Discharge Scale for older people and to reduce the scale to a more practical short form. Background The Readiness for Hospital Discharge Scale is the only available and validated scale measuring patients\u27 perceived readiness just prior to discharge. Design Secondary analysis of hospital studies data from three countries. Method Data were collected between 2008–2012. The study sample comprised 998 medical-surgical older patients. Factor analysis was undertaken to identify the factor structure of the Readiness for Hospital Discharge Scale. Group comparisons for construct validity and predictive validity for readmission were also conducted. Results The Readiness for Hospital Discharge Scale original four factor solution does not appear to be consistent with the observed data of older people in the three countries. Confirmatory factor analysis revealed that a 17-item scale with three factors produced the best model fit. Nine items, three from each factor, loaded consistently on their respective factors in each country sample. Confirmatory factor analysis of this short form model indicated that the model adequately fit the data. Patients who lived alone, were older, or who indicated ‘not ready’ for discharge had lower Readiness for Hospital Discharge Scale for Older People scores, which were also associated with readmission risk. Conclusion The revised three factor structure of the Readiness for Hospital Discharge Scale for Older People in long and short forms more adequately assesses core components of discharge readiness in the older adult population than the original adult form

Assessing Hedge Fund Performance: Does the Choice of Measures Matter?

In this paper, we conducted a comparative study of ten measures documented as the most used by researchers and practionners: Sharpe, Sortino, Calmar, Sterling, Burke, modified Stutzer, modified Sharpe, upside potential ratio, Omega and AIRAP. This study was carried out in two stages on a sample of 149 hedge funds. First, we examined the modifications of funds' relative performance in terms of ranks and deciles when the performance measure changes. Despite strong positive correlations between funds' rankings established by different measures, numerous significant modifications were observed. Second, we studied the stability/persistence of the ten measures in question. Our results show that some measures are more stable or persistent than the others in measuring hedge fund performance.hedge funds; performance evaluation; performance measure; Sharpe ratio