An evaluation of the impact of industrial restructuring on individual human capital accumulation in France (1956-1993)

This article evaluates the effect of French industrial restructuring during 1956-1993, on individual human capital accumulation. We use data from the French Training and Occupational Skills survey and the Population Census (INSEE). We estimate a human capital production function using two econometric strategies (controlling for covariates; instrumental variables). We show that industrial restructuring has a negative impact on individual human capital accumulation for the children of blue-collar workers

Generalized Fokker-Planck equation for piecewise-diffusion processes with boundary hitting resets

13 pages, 2 figuresInternational audienceThis paper is concerned with the generalized Fokker-Planck equation for a class of stochastic hybrid processes, where diffusion and instantaneous jumps at the boundary are allowed. The state of the process after a jump is defined by a deterministic reset map. We establish a partial differential equation for the probability density function, which is a generalisation of the usual Fokker-Planck equation for diffusion processes. The result involves a non-local boundary condition, which accounts for the jumping behaviour of the process, and an absorbing boundary condition on the non-characteristic part of the boundary. Two applications are given, with numerical results obtained by finite volume discretization

Kriging for indirect measurement, with application to flow measurement

International audienceKriging, a technique originating from geostatistics, is employed to build black-box models to be used to predict a quantity of interest based on the values taken by some experimental factors. This attractive alternative to more popular techniques such as neural networks is first presented. It is then applied to the measurement of the flow in a water pipe from the observation of speed at given points of a cross section. A pure black-box model turns out not to be satisfactory, and two approaches are suggested for incorporating prior knowledge. The second one, which is more systematic also turns out to provide much better performance

Identifcation stable et reconstruction robuste de signaux non stationnaires à échantillons manquants

National audienceOn souhaite reconstruire en ligne un signal à échantillons manquants. Lorsque la perte est élevée les méthodes existantes peuvent conduire à l'identifcation de modèles instables. Nous proposons, à notre connaissance, le premier algorithme qui permet le traitement en ligne des signaux à échantillons manquants utilisant la structure en treillis du filtre. La robustesse à un fort taux de perte et la stabilité du modèle ainsi identifié sont garanties. Les performances de ce nouvel algorithme dépassent celles des algorithmes existants et ce d'autant plus que la probabilité de perte est forte

Relabeling and Summarizing Posterior Distributions in Signal Decomposition Problems when the Number of Components is Unknown

International audienceThis paper addresses the problems of relabeling and summarizing posterior distributions that typically arise, in a Bayesian framework, when dealing with signal decomposition problems with an unknown number of components. Such posterior distributions are defined over union of subspaces of differing dimensionality and can be sampled from using modern Monte Carlo techniques, for instance the increasingly popular RJ-MCMC method. No generic approach is available, however, to summarize the resulting variable-dimensional samples and extract from them component-specific parameters. We propose a novel approach, named Variable-dimensional Approximate Posterior for Relabeling and Summarizing (VAPoRS), to this problem, which consists in approximating the posterior distribution of interest by a "simple"---but still variable-dimensional---parametric distribution. The distance between the two distributions is measured using the Kullback-Leibler divergence, and a Stochastic EM-type algorithm, driven by the RJ-MCMC sampler, is proposed to estimate the parameters. Two signal decomposition problems are considered, to show the capability of VAPoRS both for relabeling and for summarizing variable dimensional posterior distributions: the classical problem of detecting and estimating sinusoids in white Gaussian noise on the one hand, and a particle counting problem motivated by the Pierre Auger project in astrophysics on the other hand

Summarizing Posterior Distributions in Signal Decomposition Problems when the Number of Components is Unknown

International audienceThis paper addresses the problem of summarizing the posterior distributions that typically arise, in a Bayesian framework, when dealing with signal decomposition problems with unknown number of components. Such posterior distributions are defined over union of subspaces of differing dimensionality and can be sampled from using modern Monte Carlo techniques, for instance the increasingly popular RJ-MCMC method. No generic approach is available, however, to summarize the resulting variable-dimensional samples and extract from them component-specific parameters. We propose a novel approach to this problem, which consists in approximating the complex posterior of interest by a "simple"--but still variable-dimensional--parametric distribution. The distance between the two distributions is measured using the Kullback- Leibler divergence, and a Stochastic EM-type algorithm, driven by the RJ-MCMC sampler, is proposed to estimate the parameters. The proposed algorithm is illustrated on the fundamental signal processing example of joint detection and estimation of sinusoids in white Gaussian noise

On the joint Bayesian model selection and estimation of sinusoids via reversible jump MCMC in low SNR situations

This paper addresses the behavior in low SNR situations of the algorithm proposed by Andrieu and Doucet (IEEE T. Signal Proces., 47(10), 1999) for the joint Bayesian model selection and estimation of sinusoids in Gaussian white noise. It is shown that the value of a certain hyperparameter, claimed to be weakly influential in the original paper, becomes in fact quite important in this context. This robustness issue is fixed by a suitable modification of the prior distribution, based on model selection considerations. Numerical experiments show that the resulting algorithm is more robust to the value of its hyperparameters

NEW FAST RECURSIVE ALGORITHMS FOR SIMULTANEOUS RECONSTRUCTION AND IDENTIFICATION OF AR PROCESSES WITH MISSING OBSERVATIONS

This paper deals with the problem of adaptive reconstruction and identification of AR processes with randomly missing observations. The performances of a previously proposed real time algorithm are studied. Two new alternatives, based on other predictors, are proposed. They offer an unbiased estimation of the AR parameters. The first algorithm, based on the h-step predictor, is very simple but suffers from a large reconstruction error. The second one, based on the incomplete past predictor, offers an optimal reconstruction error in the least mean square sense

NEW FAST ALGORITHM FOR SIMULTANEOUS IDENTIFICATION AND OPTIMAL RECONSTRUCTION OF NON STATIONARY AR PROCESSES WITH MISSING OBSERVATIONS

International audienceThis paper deals with the problem of adaptive reconstruction and identification of AR processes with randomlymissing observations. A new real time algorithm is proposed. It uses combined pseudo-linear RLS algorithm and Kalman filter. It offers an unbiased estimation of the AR parameters and an optimal reconstruction error in the least mean square sense. In addition, thanks to the pseudo-linear RLS identification, this algorithm can be used for the identification of non stationary AR signals. Moreover, simplifications of the algorithm reduces the calculation time, thus this algorithm can be used in real time applications

Adaptive transmission for lossless image reconstruction

International audienceThis paper deals with the problem of adaptive digital transmission systems for lossless reconstruction. A new system, based on the principle of non-uniform transmission, is proposed. It uses a recently proposed algorithm for adaptive stable identification and robust reconstruction of AR processes subject to missing data. This algorithm offers at the same time an unbiased estimation of the model's parameters and an optimal reconstruction in the least mean square sense. It is an extension of the RLSL algorithm to the case of missing observations combined with a Kalman filter for the prediction. This algorithm has been extended to 2D signals. The proposed method has been applied for lossless image compression. It has shown an improvement in bit rate transmission compared to the JPEG2000 standard