Metamodeling of aircraft infrared signature dispersion

Existing computer simulations of aircraft InfraRed Signature (IRS) do not account for the dispersion induced by uncertainty on input data such as aircraft aspect angles and meteorological conditions. As a result, they are of little use to estimate the detection performance of optronic systems: in that case, the scenario encompasses a lot of possible situations that must indeed be addressed, but cannot be singly simulated. In this paper, a three-step methodological approach for predicting simulated IRS dispersion of imperfectly known aircraft is proposed. The first step is a sensitivity analysis. The second step consists in a Quasi-Monte Carlo survey of the code output dispersion. In the last step, a neural network metamodel of the IRS simulation code is constructed. It will allow carrying out thorough computationally demanding tasks, such as those required for optimization of an optronic sensor. This method is illustrated in a typical scenario, namely an air-to-ground full-frontal attack by a generic combat aircraft, and gives satisfactory estimation of the infrared signature dispersion.     

Adaptive sequential design for regression on multi-resolution bases

This work investigates the problem of construction of designs for estimation and discrimination between competing linear models. In our framework, the unknown signal is observed with the addition of a noise and only a few evaluations of the noisy signal are available. The model selection is performed in a multi-resolution setting. In this setting, the locations of discrete sequential D and A designs are precisely constraint in a small number of explicit points. Hence, an efficient stochastic algorithm can be constructed that alternately improves the design and the model. Several numerical experiments illustrate the efficiency of our method for regression. One can also use this algorithm as a preliminary step to build response surfaces for sensitivity analysis.     

The Contribution of Irish Immigrants to the Quebec (Canada) Gene Pool: An Estimation Using Data from Deep-Rooted Genealogies

European settlement in Quebec (Canada) began in the early 17th century, with the arrival of French pioneers. After the British Conquest in 1760, immigrants from the British Isles began to settle in some parts of Quebec. Many of these immigrants were Irish Catholics. Historians and genealogists have identified several names of Irish origin in the French Canadian population, and many scholars have wondered about the importance of the integration of Irish migrants and their descendants within this population. The purposes of this study are to identify and characterize the founders of Irish origin to estimate the importance of their genetic contribution to the contemporary Quebec population, and to measure the variability of this contribution according to the founders’ period of arrival and county of origin in Ireland. Data was obtained from a set of 2,223 ascending genealogies going back as far as the early 17th century. The average genealogical depth is a little more than 9 generations, with many branches reaching 16 or 17 generations. Although Irish founders explain less than 1% of the total Quebec gene pool, results show that nearly 21% of the genealogies contain at least one Irish founder. These founders contributed to the peopling of all regions of Quebec, but there are some important variations from one region to another. A majority of the Irish founders immigrated during the 19th century, and most of them came from the counties of Southern Ireland.     

Societal Transition, Policy Changes and Family Formation: Evidence from Hungary

The paper uses the Hungarian Generations and Gender Survey ‘Turning Points in the Life-course’ (HGGS) to describe recent changes in union formation, onset of childbearing, leaving home and cohabitation. By estimating survivor functions and semi-parametric hazard regression models with time-varying covariates for the timing of first union and first birth, we find a long delay among the youngest cohorts, but also remarkably strong period effects. Reduced employment, increased educational enrolment, and a higher level of uncertainty are important drivers behind these changes. Moreover, our evidence suggests that certain policy changes during the transition have exacerbated this effect, having an asymmetric impact on family formation – depending on the social status of individuals.Aassve, A., Billari, F. C. et Spéder, Zs., 2006. Société en transition, changements de politiques et formation de la famille: le cas de la Hongrie. Revue europé enne de démographie 22: 127–152.     

Pension design and gender: Analyses of developed and developing countries

In this article the authors examine different ways of organizing and financing pension systems in China, France, Ghana, Jordan, Mexico, Poland, and Sweden. They explore the advantages and disadvantages of the combinations of different features with special reference to gender differences. Men and women have different patterns of work history, with women usually having a lower participation rate in the formal labor market, including interrupted career in response to child rearing, as well as lower wages in general. Women have a longer life expectancy than men and are more likely to become widows than men are to become widowers. These differences influence the financial resources available to women in old age, depending on how a pension system is designed.     

A Bayesian analysis of directional data using the projected normal distribution

This paper presents a Bayesian analysis of the projected normal distribution, which is a flexible and useful distribution for the analysis of directional data. We obtain samples from the posterior distribution using the Gibbs sampler after the introduction of suitably chosen latent variables. The procedure is illustrated using simulated data as well as a real data set previously analysed in the literature.     

Importance sampling with the generalized exponential power density

In this paper, the generalized exponential power (GEP) density is proposed as an importance function in Monte Carlo simulations in the context of estimation of posterior moments of a location parameter. This density is divided in five classes according to its tail behaviour which may be exponential, polynomial or logarithmic. The notion of p-credence is also defined to characterize and to order the tails of a large class of symmetric densities by comparing their tails to those of the GEP density.The choice of the GEP density as an importance function allows us to obtain reliable and effective results when p-credences of the prior and the likelihood are defined, even if there are conflicting sources of information. Characterization of the posterior tails using p-credence can be done. Hence, it is possible to choose parameters of the GEP density in order to have an importance function with slightly heavier tails than the posterior. Simulation of observations from the GEP density is also addressed.     

Preliminary phi-divergence test estimators for linear restrictions in a logistic regression model

The problem of estimation of the parameters in a logistic regression model is considered under multicollinearity situation when it is suspected that the parameter of the logistic regression model may be restricted to a subspace. We study the properties of the preliminary test based on the minimum ϕ -divergence estimator as well as in the ϕ -divergence test statistic. The minimum ϕ -divergence estimator is a natural extension of the maximum likelihood estimator and the ϕ -divergence test statistics is a family of the test statistics for testing the hypothesis that the regression coefficients may be restricted to a subspace.     

Calculation of the Prokhorov distance by optimal quantization and maximum flow

Calculating exact values of the Prokhorov metric for the set of probability distributions on a metric space is a challenging problem. In this paper probability distributions are approximated by finite-support distributions through optimal or quasi-optimal quantization, in such a way that exact calculation of the Prokhorov distance between a distribution and a quantizer can be performed. The exact value of the Prokhorov distance between two quantizers is obtained by solving an optimization problem through the Simplex method. This last value is used to approximate the Prokhorov distance between the two initial distributions, and the accuracy of the approximation is measured. We illustrate the method on various univariate and bivariate probability distributions. Approximation of bivariate standard normal distributions by quasi-optimal quantizers is also considered.