Simulation of right and left truncated gamma distributions by mixtures

We study the properties of truncated gamma distributions and we derive simulation algorithms which dominate the standard algorithms for these distributions. For the right truncated gamma distribution, an optimal accept–reject algorithm is based on the fact that its density can be expressed as an infinite mixture of beta distribution. For integer values of the parameters, the density of the left truncated distributions can be rewritten as a mixture which can be easily generated. We give an optimal accept–reject algorithm for the other values of the parameter. We compare the efficiency of our algorithm with the previous method and show the improvement in terms of minimum acceptance probability. The algorithm proposed here has an acceptance probability which is superior to e/4.     

On the Validity of the Bootstrap in Non‐Parametric Functional Regression

Abstract. We consider the functional non‐parametric regression model Y= r( χ )+?, where the response Y is univariate, χ is a functional covariate (i.e. valued in some infinite‐dimensional space), and the error ? satisfies E(? | χ ) = 0. For this model, the pointwise asymptotic normality of a kernel estimator of r (·) has been proved in the literature. To use this result for building pointwise confidence intervals for r (·), the asymptotic variance and bias of need to be estimated. However, the functional covariate setting makes this task very hard. To circumvent the estimation of these quantities, we propose to use a bootstrap procedure to approximate the distribution of . Both a naive and a wild bootstrap procedure are studied, and their asymptotic validity is proved. The obtained consistency results are discussed from a practical point of view via a simulation study. Finally, the wild bootstrap procedure is applied to a food industry quality problem to compute pointwise confidence intervals.     

Data-Driven Model Choice in Multivariate Nonparametric Regression

The problem of choosing between different regression models, is investigated. A completely automatic model selection procedure is defined and asymptotic optimality results are shown. These results are stated in a general framework including as well nested as unnested situations, including as well i.i.d. as dependent samples, and without specifying any class of non-parametric smoothers. Because of the well-known curse of dimensionality, model selection is of particular interest in multivariate regression settings, and it will be discussed how the previous general model choice approach can be used in several different highdimensional situations.     

Modeling Multisite Activity from Occupational Surveys: Deducing the Number of Anesthesiologists from a Count of Anesthesiology Posts in France

Reliable data on the total number of physicians practicing in France cannot be obtained from national professional or administrative files which are not kept to date. From a public health standpoint, the total number of physicians' posts in establishments matters more than the actual total number of physicians, although the latter is necessary for demographic projections and to determine training needs. Shared work in the same post is sometimes encouraged to avoid closure of the post or even of the establishment. The relationship between the total number of posts in an occupation and the total number of individuals working in those posts depend on the duration of a reference period for the enumeration. An original model of this relationship is developed and applied to a count of anesthesiology posts in France with different values of the reference period. Instead of the usual question “How many anesthesiologists are currently working in your hospital?” the model suggests that the right question is “How many anesthesiologists have been working in your institute since the last 2.5 weeks?”. The total number of anesthesia and intensive care practitioners estimated by the model is 8,992 which is lower than the direct count of 9,741 posts.     

Constructing climate capitalism: corporate power and the global climate policy‐planning network

In this article, I analyse the corporate hegemonic structures of power underlying the project of climate capitalism. I present climate capitalism as an emerging regime of accumulation founded on carbon markets and the ecological modernization of production, which could replace the prevalent carboniferous capitalist regime and provide a deeply needed reduction of carbon emissions. I map out the network of corporate‐funded climate and environmental policy groups participating in climate capitalist knowledge production and mobilization to provide a critical appraisal of the possibility of such a transition. The positioning of these policy groups allows them to play a crucial role as intermediaries between regional and sectoral corporate interests and they provide a crucial link between energy and financial firms. However, energy–finance linkages are sparse, and a small number of individual capitalists carry a relatively thin network from the fossil fuel and nuclear sectors. These findings cast doubt on the hypothesis that a strong climate capitalist coalition is emerging.     

The Politics of Memory in Annexed Lorraine: The Conflicts between Germanification and French Stalwarts at the beginning of the 20th Century

The article examines how the uses of memory in turn‐of‐the‐century Lorraine structured political discourse and presented enduring difficulties for the actions of German administrators and local community leaders. In this border region, memory was always contested and challenged, and thereby unstable. This paper approaches “the politics of French memory” through the examination of various pro‐French “memory societies” and networks such as the Souvenir Français. The central question is how did conflicts over memory impact Lorraine's political life and its place in the German Empire in the years leading up to the Great War? Regarding this point, the growth of nationalism is analysed as a phenomenon that reached far beyond French nationalist circles.