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.

In defence of model-based inference in phylogeography.

Abstract: Recent papers have promoted the view that model-based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model-based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model-based inference in population genetics.

Kac’s program in kinetic theory

Abstract: This paper is devoted to the study of propagation of chaos and mean-field limits for systems of indistinguishable particles, undergoing collision processes. The prime examples we will consider are the many-particle jump processes of Kac and McKean (Kac in Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, 1954–1955, Vol. III, pp. 171–197, 1956; McKean in J. Comb. Theory 2:358–382, 1967) giving rise to the Boltzmann equation. We solve the conjecture raised by Kac (Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, 1954–1955, Vol. III, pp. 171–197, 1956), motivating his program, on the rigorous connection between the long-time behavior of a collisional many-particle system and the one of its mean-field limit, for bounded as well as unbounded collision rates.

ABC random forests for Bayesian parameter inference.

Abstract: Approximate Bayesian Computation (ABC) has grown into a standard methodology to handle Bayesian inference in models associated with intractable likelihood functions. Most ABC implementations require the selection of a summary statistic as the data itself is too large or too complex to be compared to simulated realisations from the assumed model. The dimension of this statistic is generally constrained to be close to the dimension of the model parameter for efficiency reasons. Furthermore, the tolerance level that governs the acceptance or rejection of parameter values needs to be calibrated and the range of calibration techniques available so far is mostly based on asymptotic arguments. We propose here to conduct Bayesian inference based on an arbitrarily large vector of summary statistics without imposing a selection of the relevant components and bypassing the derivation of a tolerance. The approach relies on the random forest methodology of Breiman (2001) when applied to regression. We advocate the derivation of a new random forest for each component of the parameter vector, a tool from which an approximation to the marginal posterior distribution can be derived. Correlations between parameter components are handled by separate random forests. This technology offers significant gains in terms of robustness to the choice of the summary statistics and of computing time, when compared with more standard ABC solutions.

Sudden stops in the Euro area

Abstract: The single currency was expected to make national balance of payments irrelevant for euro-area members. From 2010 onwards, however, governments, but also banks and non-financial companies in several euro-area countries have had difficulty getting access to non-resident financing. Assessing whether there has been a balance-of-payment crisis by looking at the current-account developments is a flawed approach in the case of countries that receive significant official support through assistance programmes or the Eurosystem of central banks. In this paper we document the evolution of private capital flows and formally test for the existence of ‘sudden stops’. We find that Greece, Ireland, Italy, Portugal and Spain experienced significant private-capital inflows from 2002 to 2007-09, followed by unambiguously massive outflows that qualify as ‘sudden stops’. The timeline suggests contagion effects were present. We document the substitution of the private capital flows by public flows. In particular, we show that (weak) banks in distressed countries took up a major share of central bank refinancing, thereby contributing to the build-up of intra-Eurosystem net balances. The evidence that the euro area has been subject to internal balance-of-payment crises should be taken as a strong signal of weakness and as an invitation to systemic reform.