Croissance économique,flexibilité du travail et qualité de l'emploi en Colombie de 2002 à 2011

Entre 2002 et 2011, le marché du travail colombien a connu une nette amélioration des possibilités d'emploi, assortie d'une montée de l'emploi atypique. Les auteurs étudient l'incidence de ces phénomènes sur la qualité de l'emploi au moyen d'un indice synthétique dérivé d'une analyse des correspondances multiples. Ils observent une amélioration, légère mais générale, de cette qualité, associée à l'augmentation des revenus, à l'extension de la protection sociale et au recul du sous‐emploi. Les principales bénéficiaires sont les travailleuses indépendantes. Toutefois, un important déficit qualitatif perdure, qui appelle à consolider les institutions du marché du travail et à stimuler la productivité et l'investissement.     

Bayesian approach for meta - analysis of controlled clinical trials

A new method to perform meta - analysis of controlled clinical trials with binary response variable is developed using a Bayesian approach. It consists of three parts: (1) For each trial, the risk difference (the proportion of successes in the treated group minus the proportion of successes in the control group) is estimated; (2) The homogeneity of the risk difference among the different trials is tested; and (3) The hypothesis - the effect of the treatment for the homogeneous pool of trials is greater than or equal to a given fixed constant - is tested. The performance of the Bayesian procedure to test the homogeneity of the risk difference among trials is compared with the chi - square test proposed by DerSimonian and Laird (Controlled Clinical Trials 7, 177-188, 1986) by means of pseudo - random simulation. The conclusion was that the Bayes test is more reliable, either in its exact or asymptotic versions, since it makes fewer decision errors than the chi-square test. As an illustration, the Bayesian method is applied to data of chemotherapeutic prophylaxis of superficial bladder cancer.     

Introducing model uncertainty by moving blocks bootstrap

It is common in parametric bootstrap to select the model from the data, and then treat as if it were the true model. Chatfield (1993, 1996) has shown that ignoring the model uncertainty may seriously undermine the coverage accuracy of prediction intervals. In this paper, we propose a method based on moving block bootstrap for introducing the model selection step in the resampling algorithm. We present a Monte Carlo study comparing the finite sample properties of the proposel method with those of alternative methods in the case of prediction intervas.     

What Does it Take for a Canadian Political Scientist to be Cited?*

Objectives. The article examines the factors that influence the frequency whereby scholarly articles published by Canadian political scientists are cited. Method. We collected data on 1,860 journal articles published between 1985 and 2005 by 758 Canadian political scientists and listed in the Social Science Citation Index. Using these data, we performed OLS and tobit estimations to identify factors influencing citation frequency. Results. The regressions show that the reputation of the journal in which the article is published, though important, does not explain everything. The gender of the author(s), the number of authors, the geographical focus of the article, the field, and the methodology also matter. Conclusion. An article is more likely to be widely cited if it is published in a prestigious journal, if it is written by several authors, if it applies quantitative methods, if it compares countries, and if it deals with administration and public policy or elections and political parties. Faculty members who belong to larger departments and those who are women are more cited.     

An Analysis of an Autoclitic Analogue in Pigeons

The Analysis of Verbal Behavior - Using a conditional discrimination procedure, pigeons were exposed to a nonverbal analogue of qualifying autoclitics such as definitely and maybe. It has been...     

Regularized Bayesian quantile regression

A number of nonstationary models have been developed to estimate extreme events as function of covariates. A quantile regression (QR) model is a statistical approach intended to estimate and conduct inference about the conditional quantile functions. In this article, we focus on the simultaneous variable selection and parameter estimation through penalized quantile regression. We conducted a comparison of regularized Quantile Regression model with B-Splines in Bayesian framework. Regularization is based on penalty and aims to favor parsimonious model, especially in the case of large dimension space. The prior distributions related to the penalties are detailed. Five penalties (Lasso, Ridge, SCAD0, SCAD1 and SCAD2) are considered with their equivalent expressions in Bayesian framework. The regularized quantile estimates are then compared to the maximum likelihood estimates with respect to the sample size. A Markov Chain Monte Carlo (MCMC) algorithms are developed for each hierarchical model to simulate the conditional posterior distribution of the quantiles. Results indicate that the SCAD0 and Lasso have the best performance for quantile estimation according to Relative Mean Biais (RMB) and the Relative Mean-Error (RME) criteria, especially in the case of heavy distributed errors. A case study of the annual maximum precipitation at Charlo, Eastern Canada, with the Pacific North Atlantic climate index as covariate is presented.