38 research outputs found
Sociology and political science in the patrimonial society: implications of Piketty's Capital
What are the implications of Piketty's Capital for sociology and political science? Capital's argument focuses on the evolution of the r/g ratio (capital returns over growth rate) and outlines two modes of economic inequalities. One is characteristic of affluent (g > r) societies and the other is characteristic of patrimonial (r > g) societies. With the current return to a patrimonial society, corporations become political actors; occupational status and education's relevance are declining; the meaning of poverty is transformed, and welfare and punishment become interdependent means to social order; in politics, elitist theories gain traction; immigration is less about assimilation, and more about transnationalism and nationalist politics. We show that some theories are more relevant in an affluent society, and others are more adequate to a patrimonial society
Sociology and political science in the patrimonial society: implications of Piketty's Capital
What are the implications of Piketty's Capital for sociology and political science? Capital's argument focuses on the evolution of the r/g ratio (capital returns over growth rate) and outlines two modes of economic inequalities. One is characteristic of affluent (g > r) societies and the other is characteristic of patrimonial (r > g) societies. With the current return to a patrimonial society, corporations become political actors; occupational status and education's relevance are declining; the meaning of poverty is transformed, and welfare and punishment become interdependent means to social order; in politics, elitist theories gain traction; immigration is less about assimilation, and more about transnationalism and nationalist politics. We show that some theories are more relevant in an affluent society, and others are more adequate to a patrimonial society
Ferguson et la nouvelle condition noire aux Etats-Unis
Site internet: http://www.laviedesidees.fr
Décorrélation de variables à base de modÚles en régression linéaire (CorReg). Application aux données manquantes et à l'industrie sidérurgique.
This thesis was motivated by correlation issues in real datasets, in particular industrialdatasets. The main idea stands in explicit modeling of the correlations between covariatesby a structure of sub-regressions, that simply is a system of linear regressions betweenthe covariates. It points out redundant covariates that can be deleted in a pre-selectionstep to improve matrix conditioning without significant loss of information and withstrong explicative potential because this pre-selection is explained by the structure ofsub-regressions, itself easy to interpret.An algorithm to find the sub-regressions structure inherent to the dataset is provided,based on a full generative model and using Monte-Carlo Markov Chain (MCMC) method.This pre-treatment is then applied on linear regression to show its efficiency but does notdepend on a response variable and thus can be used in a more general way with anycorrelated datasets.In a second part, a plug-in estimator is defined to get back the redundant covariatessequentially. Then all the covariates are used but the sequential approach acts as a protectionagainst correlations.Finally, the generative model defined here allows, as a perspective, to manage missingvalues both during the MCMC and then for imputation (for example multiple imputation).Then we are able to use classical methods that are not compatible with missingdatasets. Missing values can be imputed with a confidence interval to show estimationaccuracy. Once again, linear regression is used to illustrate the benefits of this methodbut it remains a pre-treatment that can be used in other contexts, like clustering and so on.The industrial motivation of this work defines interpretation as a stronghold at eachstep.The R package CorReg, is on cran3 now under CeCILL4 license. It implements themethods created during this thesis.Keywords: Pre-treatment, Regression, Correlations, Missing values, MCMC, generativemodel, Bayesian Criterion, variable selection, plug-in method,. . .corrĂ©lationsau sein des bases de donnĂ©es, particuliĂšrement frĂ©quentes dans le cadre industriel.Une modĂ©lisation explicite des corrĂ©lations par un systĂšme de sous-rĂ©gressions entre covariablespermet de pointer les sources des corrĂ©lations et dâisoler certaines variablesredondantes.Il en dĂ©coule une prĂ©-sĂ©lection de variables nettement moins corrĂ©lĂ©es sans perte significativedâinformation et avec un fort potentiel explicatif (la prĂ©-selection elle-mĂȘme estexpliquĂ©e par la structure de sous-rĂ©gression qui est simple Ă comprendre car uniquementconstituĂ©e de modĂšles linĂ©aires).Un algorithme de recherche de structure de sous-rĂ©gressions est proposĂ©, basĂ© surun modĂšle gĂ©nĂ©ratif complet sur les donnĂ©es et utilisant une chaĂźne MCMC (Monte-Carlo Markov Chain). Ce prĂ©traitement est utilisĂ© pour la rĂ©gression linĂ©aire comme uneprĂ©sĂ©lection des variables explicatives Ă des fins illustratives mais ne dĂ©pend pas de lavariable rĂ©ponse. Il peut donc ĂȘtre utilisĂ© de maniĂšre gĂ©nĂ©rale pour toute problĂ©matiquede corrĂ©lations.Par la suite, un estimateur plug-in pour la rĂ©gression linĂ©aire est proposĂ© pour rĂ©injecterlâinformation rĂ©siduelle contenue dans les variables redondantes de maniĂšre sĂ©quentielle.On utilise ainsi toutes les variables sans souffrir des corrĂ©lations entre covariables.Enfin, le modĂšle gĂ©nĂ©ratif complet offre la perspective de pouvoir ĂȘtre utilisĂ© pour gĂ©rerdâĂ©ventuelles valeurs manquantes dans les donnĂ©es. Cela permet la recherche de structuremalgrĂ© lâabsence de certaines donnĂ©es. Mais un autre dĂ©bouchĂ© est lâimputation multipledes donnĂ©es manquantes, prĂ©alable Ă lâutilisation de mĂ©thodes classiques incompatiblesavec la prĂ©sence de valeurs manquantes. De plus, lâimputation multiple des valeurs manquantespermet dâobtenir un estimateur de la variance des valeurs imputĂ©es. Encoreune fois, la rĂ©gression linĂ©aire vient illustrer lâapport de la mĂ©thode qui reste cependantgĂ©nĂ©rique et pourrait ĂȘtre appliquĂ©e Ă dâautres contextes tels que le clustering.Tout au long de ces travaux, lâaccent est mis principalement sur lâinterprĂ©tabilitĂ© desrĂ©sultats en raison du caractĂšre industriel de cette thĂšse.Le package R intitulĂ© CorReg, disponible sur le cran sous licence CeCILL, implĂ©menteles mĂ©thodes dĂ©veloppĂ©es durant cette thĂšse
Model-Based Variable Decorrelation in Linear Regression
Linear regression outcomes (estimates, prevision) are known to be damaged by highly correlated covariates. However most modern datasets are expected to mechanically convey more and more highly correlated covariates due to the global increase of the amount of variables they contain. We propose to explicitly model such correlations by a family of linear regressions between the covariates. The structure of correlations is found with an mcmc algorithm aiming at optimizing a specific bic criterion. This hierarchical-like approach leads to a joint probability distribution on both the initial response variable and the linearly explained covariates. Then, marginalisation on the linearly explained covariates produces a parsimonious correlation-free regression model from which classical procedures for estimating regression coefficient, including any variable selection procedures, can be plugged. Both simulated and real-life datasets from steel industry, where correlated variables are frequent, highlight that this proposed covariates pretreatment-like method has two essential benefits: First, it offers a real readability of the linear links between covariates; Second, it improves significantly efficiency of classical estimation/selection methods which are performed after. An r package (CorReg), available on the cran, implements this new method
b-Jet Identification in the D0 Experiment
Algorithms distinguishing jets originating from b quarks from other jet
flavors are important tools in the physics program of the D0 experiment at the
Fermilab Tevatron p-pbar collider. This article describes the methods that have
been used to identify b-quark jets, exploiting in particular the long lifetimes
of b-flavored hadrons, and the calibration of the performance of these
algorithms based on collider data.Comment: submitted to Nuclear Instruments and Methods in Physics Research
Randol Contreras, The Stickup Kids: Race, Drugs, Violence, and the American Dream (University of California Press, 2012)
Comment analyser la violence du marchĂ© de la drogueâŻ? Câest la question qui travaille The Stickup Kids, une ethnographie de Randol Contreras sur un groupe de jeunes hommes dominicains du quartier du South Bronx Ă New York dont lâactivitĂ© est de «âŻbraquer des dealers de drogueâŻÂ». Le braquage de dealers requiert lâaccomplissement de plusieurs taches successivesâŻ: repĂ©rer un dealer important, câestâĂ âdire un dealer qui achĂšte et vend en grandes quantitĂ©sâŻ; pĂ©nĂ©trer chez lui par ruse afin de le s..
Décorrélation de variables à base de modÚles en régression linéaire (CorReg). Application aux données manquantes et à l'industrie sidérurgique.
This thesis was motivated by correlation issues in real datasets, in particular industrialdatasets. The main idea stands in explicit modeling of the correlations between covariatesby a structure of sub-regressions, that simply is a system of linear regressions betweenthe covariates. It points out redundant covariates that can be deleted in a pre-selectionstep to improve matrix conditioning without significant loss of information and withstrong explicative potential because this pre-selection is explained by the structure ofsub-regressions, itself easy to interpret.An algorithm to find the sub-regressions structure inherent to the dataset is provided,based on a full generative model and using Monte-Carlo Markov Chain (MCMC) method.This pre-treatment is then applied on linear regression to show its efficiency but does notdepend on a response variable and thus can be used in a more general way with anycorrelated datasets.In a second part, a plug-in estimator is defined to get back the redundant covariatessequentially. Then all the covariates are used but the sequential approach acts as a protectionagainst correlations.Finally, the generative model defined here allows, as a perspective, to manage missingvalues both during the MCMC and then for imputation (for example multiple imputation).Then we are able to use classical methods that are not compatible with missingdatasets. Missing values can be imputed with a confidence interval to show estimationaccuracy. Once again, linear regression is used to illustrate the benefits of this methodbut it remains a pre-treatment that can be used in other contexts, like clustering and so on.The industrial motivation of this work defines interpretation as a stronghold at eachstep.The R package CorReg, is on cran3 now under CeCILL4 license. It implements themethods created during this thesis.Keywords: Pre-treatment, Regression, Correlations, Missing values, MCMC, generativemodel, Bayesian Criterion, variable selection, plug-in method,. . .corrĂ©lationsau sein des bases de donnĂ©es, particuliĂšrement frĂ©quentes dans le cadre industriel.Une modĂ©lisation explicite des corrĂ©lations par un systĂšme de sous-rĂ©gressions entre covariablespermet de pointer les sources des corrĂ©lations et dâisoler certaines variablesredondantes.Il en dĂ©coule une prĂ©-sĂ©lection de variables nettement moins corrĂ©lĂ©es sans perte significativedâinformation et avec un fort potentiel explicatif (la prĂ©-selection elle-mĂȘme estexpliquĂ©e par la structure de sous-rĂ©gression qui est simple Ă comprendre car uniquementconstituĂ©e de modĂšles linĂ©aires).Un algorithme de recherche de structure de sous-rĂ©gressions est proposĂ©, basĂ© surun modĂšle gĂ©nĂ©ratif complet sur les donnĂ©es et utilisant une chaĂźne MCMC (Monte-Carlo Markov Chain). Ce prĂ©traitement est utilisĂ© pour la rĂ©gression linĂ©aire comme uneprĂ©sĂ©lection des variables explicatives Ă des fins illustratives mais ne dĂ©pend pas de lavariable rĂ©ponse. Il peut donc ĂȘtre utilisĂ© de maniĂšre gĂ©nĂ©rale pour toute problĂ©matiquede corrĂ©lations.Par la suite, un estimateur plug-in pour la rĂ©gression linĂ©aire est proposĂ© pour rĂ©injecterlâinformation rĂ©siduelle contenue dans les variables redondantes de maniĂšre sĂ©quentielle.On utilise ainsi toutes les variables sans souffrir des corrĂ©lations entre covariables.Enfin, le modĂšle gĂ©nĂ©ratif complet offre la perspective de pouvoir ĂȘtre utilisĂ© pour gĂ©rerdâĂ©ventuelles valeurs manquantes dans les donnĂ©es. Cela permet la recherche de structuremalgrĂ© lâabsence de certaines donnĂ©es. Mais un autre dĂ©bouchĂ© est lâimputation multipledes donnĂ©es manquantes, prĂ©alable Ă lâutilisation de mĂ©thodes classiques incompatiblesavec la prĂ©sence de valeurs manquantes. De plus, lâimputation multiple des valeurs manquantespermet dâobtenir un estimateur de la variance des valeurs imputĂ©es. Encoreune fois, la rĂ©gression linĂ©aire vient illustrer lâapport de la mĂ©thode qui reste cependantgĂ©nĂ©rique et pourrait ĂȘtre appliquĂ©e Ă dâautres contextes tels que le clustering.Tout au long de ces travaux, lâaccent est mis principalement sur lâinterprĂ©tabilitĂ© desrĂ©sultats en raison du caractĂšre industriel de cette thĂšse.Le package R intitulĂ© CorReg, disponible sur le cran sous licence CeCILL, implĂ©menteles mĂ©thodes dĂ©veloppĂ©es durant cette thĂšse
Randol Contreras, The Stickup Kids: Race, Drugs, Violence, and the American Dream (University of California Press, 2012)
Comment analyser la violence du marchĂ© de la drogueâŻ? Câest la question qui travaille The Stickup Kids, une ethnographie de Randol Contreras sur un groupe de jeunes hommes dominicains du quartier du South Bronx Ă New York dont lâactivitĂ© est de «âŻbraquer des dealers de drogueâŻÂ». Le braquage de dealers requiert lâaccomplissement de plusieurs taches successivesâŻ: repĂ©rer un dealer important, câestâĂ âdire un dealer qui achĂšte et vend en grandes quantitĂ©sâŻ; pĂ©nĂ©trer chez lui par ruse afin de le s..
Alice Goffman, On the run: Fugitive life in an American City (University of Chicago Press, 2014)
Traitant sous un jour nouveau de lâincarcĂ©ration de masse des jeunes hommes Noirs aux ĂtatsâUnis, On the Run est le premier livre de la jeune sociologue Alice Goffman. FondĂ© sur six annĂ©es dâimmersion ethnographique dans un quartier Noir et pauvre de Philadelphie (6th street, le quartier de la sixiĂšme rue), avec un focus sur la vie dâune dizaine de jeunes hommes Noirs, dâĂąge et dâimplications dans le crime variables (the 6th street boys, le gang de la sixiĂšme rue), le livre a reçu dâabord un ..