Statistical Estimation for a Class of Self‐Regulating Processes

Self‐regulating processes are stochastic processes whose local regularity, as measured by the pointwise Hölder exponent, is a function of amplitude. They seem to provide relevant models for various signals arising for example in geophysics or biomedicine. We propose in this work an estimator of the self‐regulating function (that is, the function relating amplitude and Hölder regularity) of the self‐regulating midpoint displacement process and study some of its properties. We prove that it is almost surely convergent and obtain a central limit theorem. Numerical simulations show that the estimator behaves well in practice.     

What is ethnic in an ethnic economy?

This article examines critically the relationship between ethnicity and entrepreneurship in the sociology of immigrant economies. It argues that what is ethnic in an ethnic economy has often been confusingly conceptualised and that several factors now call for re-assessing the ethnic nature of immigrants' business activities. On the basis of a review of recent research, three such factors are outlined: the porosity of ethnic boundaries to cross-group business interactions; the diversity within immigrant economies in terms of status, gender, class and generation; and the political and institutional context in which immigrant economies take place. The conclusion stresses the need for multiple explanations of how and why immigrants become entrepreneurs, which take into account not only the meso-level constituted by ethnicity and social capital, but also micro-individual factors and macro-institutional settings.     

On the most imbalanced orientation of a graph

We study the problem of orienting the edges of a graph such that the minimum over all the vertices of the absolute difference between the outdegree and the indegree of a vertex is maximized. We call this minimum the imbalance of the orientation, i.e. the higher it gets, the more imbalanced the orientation is. The studied problem is denoted by \({{\mathrm{\textsc {MaxIm}}}}\). We first characterize graphs for which the optimal objective value of \({{\mathrm{\textsc {MaxIm}}}}\) is zero. Next we show that \({{\mathrm{\textsc {MaxIm}}}}\) is generally NP-hard and cannot be approximated within a ratio of \(\frac{1}{2}+\varepsilon \) for any constant \(\varepsilon >0\) in polynomial time unless \(\texttt {P}=\texttt {NP}\) even if the minimum degree of the graph \(\delta \) equals 2. Then we describe a polynomial-time approximation algorithm whose ratio is almost equal to \(\frac{1}{2}\). An exact polynomial-time algorithm is also derived for cacti. Finally, two mixed integer linear programming formulations are presented. Several valid inequalities are exhibited with the related separation algorithms. The performance of the strengthened formulations is assessed through several numerical experiments.     

Discount rate heterogeneity among older households: a puzzle?

We put forward a method for estimating discount rates using wealth and income data. We build consumption from these data using the budget constraint. Consumption transitions yield discount rates by household groups. Applying this technique to a sample of older households, we find a similar distribution to those previously estimated using field data, though with a much lower mean than those found using experiments. Surprisingly, among this older population, patience is negatively correlated with education and numeracy. This goes against the positive correlation found for younger populations in experiments and some field studies. We discuss potential explanations for this result.     

Risk Perception of the "Mad Cow Disease" in France: Determinants and Consequences

Since 1996, when bovine spongiform encephalopathy (BSE) was assessed as a possible human transmissible disease, a variant of Creutzfeldt-Jakob disease (vCJD), French people have entered into a long period of fear and avoidance of beef and bovine byproducts, which produced an unprecedented collapse in the beef market. This article deals with the perceived risk of the "mad cow disease" (MCD) in the French general population. Two surveys were conducted on a representative sample of the adult population, the first one in 2000 during the peak of the crisis and the second one 13 months later in a quieter period. The main assumption we made was that changes in beef consumption are strongly related to the perceived risk of MCD, which we defined as people's cognitive and affective responses to hazard. Our objective was to identify the determinants and consequences of this perceived risk and to compare them in different sociopolitical contexts. The results issued from a bivariate and multivariate analysis show that: (i) the distribution of most of the variables significantly related to the perceived risk identified in the first survey had changed in the second survey, in relation with the reduction of worry and the resumption of national beef consumption; (ii) the propensity for self-protection through avoiding or ceasing beef eating was more related to feelings of worry than to subjective vCJD risk assessments; and (iii) the main determinant of less avoidance to beef products was the preference for beef, a feeling identified prior to emergence of the risk of MCD, remaining unchanged in various contexts.     

Probabilities as Similarity‐Weighted Frequencies

A decision maker is asked to express her beliefs by assigning probabilities to certain possible states. We focus on the relationship between her database and her beliefs. We show that if beliefs given a union of two databases are a convex combination of beliefs given each of the databases, the belief formation process follows a simple formula: beliefs are a similarity‐weighted average of the beliefs induced by each past case.     

Self-Employment and Immigrants' Incorporation: The Case of Turks in Germany

This essay discusses the impact of self-employment on immigrants' incorporation in the host society. It focuses on empirical material coming from Turkish migrants' business activities in Germany. After introducing the main features of contemporary German-Turkish entrepreneurship, the essay presents its socio-economic and cultural consequences for the Turkish minority. Relying on the numerous debates that can be found in the literature, it argues that this impact is ambivalent. In some cases, business ownership may constitute a way of achieving upward social mobility and a path towards a harmonious and pluralistic integration. But it is also frequently a reaction to disadvantage that fosters immigrants' socio-professional vulnerability and that may then lead to ghettoization. The essay then attempts to understand the reasons behind these divergent interpretations and suggests that they lie in the intermediary nature of trade, which is an activity that may connect people from different backgrounds but only in a limited way. Ethnographic observations and the concept of 'plural societies' are used to address this aspect of business. The conclusion suggests that, given the globally inferior position of Turkish immigrants, business might not be enough to enable them to catch up.     

Nonparametric regression estimation of conditional tails: the random covariate case

We present families of nonparametric estimators for the conditional tail index of a Pareto-type distribution in the presence of random covariates. These families are constructed from locally weighted sums of power transformations of excesses over a high threshold. The asymptotic properties of the proposed estimators are derived under some assumptions on the conditional response distribution, the weight function and the density function of the covariates. We also introduce bias-corrected versions of the estimators for the conditional tail index, and propose in this context a consistent estimator for the second-order tail parameter. The finite sample performance of some specific examples from our classes of estimators is illustrated with a small simulation experiment.     

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.