Extensions of stability selection using subsamples of observations and covariates

We introduce extensions of stability selection, a method to stabilise variable selection methods introduced by Meinshausen and B\"uhlmann (J R Stat Soc 72:417-473, 2010). We propose to apply a base selection method repeatedly to random observation subsamples and covariate subsets under scrutiny, and to select covariates based on their selection frequency. We analyse the effects and benefits of these extensions. Our analysis generalizes the theoretical results of Meinshausen and B\"uhlmann (J R Stat Soc 72:417-473, 2010) from the case of half-samples to subsamples of arbitrary size. We study, in a theoretical manner, the effect of taking random covariate subsets using a simplified score model. Finally we validate these extensions on numerical experiments on both synthetic and real datasets, and compare the obtained results in detail to the original stability selection method.Comment: accepted for publication in Statistics and Computin

The level and age pattern of mortality in Bandafassi (Eastern Senegal): results from a small-scale and intensive multi-round survey

The data collected by the Bandafassi demographic study in Eastern Senegal, a small-scale intensive and experimental follow-up survey on a population of about 7,000 inhabitants in 1983, were analyzed to derive an estimation of the life table. The use of the multi-round survey technique, combined with anthropological methods to estimate the ages or collect genealogies, results in unusually reliable data. Taking into account the uncertainty of the estimates related to the small size of the population, the measures of mortality show a high mortality level, with life-expectancy at birth close to 31 years; a pattern of infant and child mortality close to what has been observed in other rural areas of Senegal; a seasonal pattern in child mortality with two high risk periods, the rainy season and the end of the dry season; an adult mortality pattern similar to what is described in model life tables for developed countries; no significant differences according to sex or ethnic group. The example of the Bandafassi population study and of a few similar studies, suggests that one possible way to improve demographic estimates in countries where vital registration systems are defective would be to set up a sample of population laboratories where intensive methods of data collection would continue for extended periods

Assistance au test de modèles à composants et services

ISBN 978-2-905267-89-4International audienceDans l'ingénierie des modèles, la correction des modèles est essentielle. Tester le plus tôt possible permet de réduire le coût du processus de véri cation et de validation. Distinguer modèle abstrait et modèle spéci fique réduit la complexité du test et favorise l'évolution du système modélisé. Nous ciblons les modèles à composants et services, ayant un niveau de description su ffisamment précis et détaillé pour pouvoir exécuter les tests. Notre objectif est de tester ces modèles à composants c'est-à-dire de concevoir des cas de tests, de les appliquer sur les modèles mis dans un contexte adéquat pour être exécutés et obtenir un verdict. Pour réduire l'eff ort de construction du harnais de test, nous proposons une méthode qui guide le testeur dans le processus de conception des tests au niveau du modèle. L'assistance à la construction est basée sur (1) la détection d'incohérences et d'incomplétude entre le harnais et le modèle de test ainsi que sur (2) des propositions générant les éléments manquants. Le programme de test est alors transformé vers une plateforme technique dédiée à l'exécution des tests. La mise en oeuvre est réalisée avec des plugins Eclipse dans COSTO, une plateforme dédiée au modèle à composants Kmelia

Using Event-B to Verify the Kmelia Components and Their Assemblies

International audienceBuilding reliable software systems from components requires to verify the consistency of components and the correctness of component assemblies. In this work, we design a verification method to address the problem of verifying the consistency of components states and the correctness of assembly contracts, using pre-/post-conditions. The starting point is specifications written with the Kmelia component model: a Kmelia component type declares provided and required services which are used to link components in component assemblies. We generate Event-B models from Kmelia specifications in such a way that we can check the consistency and also the correctness of assembly at the Kmelia level, using Event-B provers. An illustrative example based on a stock management system is used to support the presentation

Using Assertions to Enhance the Correctness of Kmelia Components and their Assemblies

15 p.International audienceThe Kmelia component model is an abstract formal component model based on services. It is dedicated to the specification and development of correct components. This work enriches the Kmelia language to allow the description of data, expressions and assertions when specifying components and services. The objective is to enable the use of assertions in \kmelia in order to support expressive service descriptions, to support client/supplier contracts with pre/post-conditions, and to enhance formal analysis of component-based system. Assertions are used to perfom analysis of services, component assemblies and service compositions. We illustrate the work with the verification of consistency properties involving data at component and assembly levels

Cancer evolution: mathematical models and computational inference.

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.FM would like to acknowledge the support of The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited.This is the final published version. It first appeared at http://sysbio.oxfordjournals.org/content/early/2014/10/07/sysbio.syu081.short?rss=1