Paris Dauphine University

About: Paris Dauphine University is a education organization based out in Paris, France. It is known for research contribution in the topics: Context (language use) & Population. The organization has 1766 authors who have published 6909 publications receiving 162747 citations. The organization is also known as: Paris Dauphine & Dauphine.

Gender differences in hospital mortality and use of percutaneous coronary intervention in acute myocardial infarction : Microsimulation analysis of the 1999 nationwide french hospitals database

Abstract: Background— Women with acute myocardial infarction have a higher hospital mortality rate than men. This difference has been ascribed to their older age, more frequent comorbidities, and less freque...

SMC^2: an efficient algorithm for sequential analysis of state-space models

Abstract: We consider the generic problem of performing sequential Bayesian inference in a state-space model with observation process y, state process x and fixed parameter theta. An idealized approach would be to apply the iterated batch importance sampling (IBIS) algorithm of Chopin (2002). This is a sequential Monte Carlo algorithm in the theta-dimension, that samples values of theta, reweights iteratively these values using the likelihood increments p(y_t|y_1:t-1, theta), and rejuvenates the theta-particles through a resampling step and a MCMC update step. In state-space models these likelihood increments are intractable in most cases, but they may be unbiasedly estimated by a particle filter in the x-dimension, for any fixed theta. This motivates the SMC^2 algorithm proposed in this article: a sequential Monte Carlo algorithm, defined in the theta-dimension, which propagates and resamples many particle filters in the x-dimension. The filters in the x-dimension are an example of the random weight particle filter as in Fearnhead et al. (2010). On the other hand, the particle Markov chain Monte Carlo (PMCMC) framework developed in Andrieu et al. (2010) allows us to design appropriate MCMC rejuvenation steps. Thus, the theta-particles target the correct posterior distribution at each iteration t, despite the intractability of the likelihood increments. We explore the applicability of our algorithm in both sequential and non-sequential applications and consider various degrees of freedom, as for example increasing dynamically the number of x-particles. We contrast our approach to various competing methods, both conceptually and empirically through a detailed simulation study, included here and in a supplement, and based on particularly challenging examples.

An Emic Approach to Intersectional Study of Diversity at Work: A Bourdieuan Framing

Abstract: This paper presents an emic approach, which is sensitive to the emergence of new categories of difference, in intersectional study of workforce diversity. The paper first provides a comprehensive review of the literature on diversity at work in the business and management field, identifying that this literature is predominantly etic in nature, as it focuses on pre-established, rather than emergent, categories of difference. Next, an emic approach to researching diversity at work is offered. In offering an emic approach, the key distinction the paper makes is the direction of the investigation. Unlike the dominant etic approach, which adopts pre-established (ex ante) diversity categories, the emic perspective proposed identifies emergent and situated categories of diversity ex post, as embedded in a specific time and place. In order to operationalize the emic approach, the use of the Bourdieuan theory of capitals is suggested, and a five-step research guide is presented.

A Short Introduction to Computational Social Choice

Abstract: Computational social choice is an interdisciplinary field of study at the interface of social choice theory and computer science, promoting an exchange of ideas in both directions. On the one hand, it is concerned with the application of techniques developed in computer science, such as complexity analysis or algorithm design, to the study of social choice mechanisms, such as voting procedures or fair division algorithms. On the other hand, computational social choice is concerned with importing concepts from social choice theory into computing. For instance, the study of preference aggregation mechanisms is also very relevant to multiagent systems. In this short paper we give a general introduction to computational social choice, by proposing a taxonomy of the issues addressed by this discipline, together with some illustrative examples and an (incomplete) bibliography.