Practical targeted learning from large data sets by survey sampling

We address the practical construction of asymptotic confidence intervals for smooth (i.e., path-wise differentiable), real-valued statistical parameters by targeted learning from independent and identically distributed data in contexts where sample size is so large that it poses computational challenges. We observe some summary measure of all data and select a sub-sample from the complete data set by Poisson rejective sampling with unequal inclusion probabilities based on the summary measures. Targeted learning is carried out from the easier to handle sub-sample. We derive a central limit theorem for the targeted minimum loss estimator (TMLE) which enables the construction of the confidence intervals. The inclusion probabilities can be optimized to reduce the asymptotic variance of the TMLE. We illustrate the procedure with two examples where the parameters of interest are variable importance measures of an exposure (binary or continuous) on an outcome. We also conduct a simulation study and comment on its results. keywords: semiparametric inference; survey sampling; targeted minimum loss estimation (TMLE

The Effects of a "Fat Tax" on the Nutrient Intake of French Households

This article assesses the effects of a "fat tax" on the nutrient intake of French households across different income groups using a method that estimates the nutrient elasticities of French households. We estimate a complete demand system by aggregating an individual demand system over cohorts. The use of a cohort model is justified by the incompleteness of our data. We find that a "fat tax" would have ambiguous and extremely small effects on the nutrient intake of French households, and its associated economic welfare costs would be similarly weak.Household survey data, demand system, nutrient elasticities., Food Consumption/Nutrition/Food Safety,

Bootstrapping Quasi Likelihood Ratio Tests under Misspecification

We consider quasi likelihood ratio (QLR) tests for restrictions on parameters under potential model misspecification. For convex M-estimation, including quantile regression, we propose a general and simple nonparametric bootstrap procedure that yields asymptotically valid critical values. The method modifies the bootstrap objective function to mimic what happens under the null hypothesis. When testing for an univariate restriction, we show how the test statistic can be made asymptotically pivotal. Our bootstrap can then provide asymptotic refinements as illustrated for a linear regression model. A Monte-Carlo study and an empirical application illustrate that double bootstrap of the QLR test controls level well and is powerful

Consistency of the Frequency Domain Bootstrap for differentiable functionals

In this paper consistency of the Frequency Domain Bootstrap for differentiable functionals of spectral density function of a linear stationary time series is discussed. The notion of influence function in the time domain on spectral measures is introduced. Moreover, the Fréchet differen-tiability of functionals of spectral measures is defined. Sufficient and necessary conditions for consistency of the FDB in the considered problems are provided and the second order correctness is discussed for some functionals. Finally, validity of the FDB for the empirical processes is considered

On Medians of (Randomized) Pairwise Means

Tournament procedures, recently introduced in Lugosi & Mendelson (2016), offer an appealing alternative, from a theoretical perspective at least, to the principle of Empirical Risk Minimization in machine learning. Statistical learning by Median-of-Means (MoM) basically consists in segmenting the training data into blocks of equal size and comparing the statistical performance of every pair of candidate decision rules on each data block: that with highest performance on the majority of the blocks is declared as the winner. In the context of nonparametric regression, functions having won all their duels have been shown to outperform empirical risk minimizers w.r.t. the mean squared error under minimal assumptions, while exhibiting robustness properties. It is the purpose of this paper to extend this approach in order to address other learning problems, in particular for which the performance criterion takes the form of an expectation over pairs of observations rather than over one single observation, as may be the case in pairwise ranking, clustering or metric learning. Precisely, it is proved here that the bounds achieved by MoM are essentially conserved when the blocks are built by means of independent sampling without replacement schemes instead of a simple segmentation. These results are next extended to situations where the risk is related to a pairwise loss function and its empirical counterpart is of the form of a $U$-statistic. Beyond theoretical results guaranteeing the performance of the learning/estimation methods proposed, some numerical experiments provide empirical evidence of their relevance in practice

Les faibles effets d’une « fat tax » sur les achats alimentaires des ménages français : une approche par les nutriments

L’Organisation Mondiale de la Santé considère le surpoids et l’obésité comme un des problèmes majeurs de santé publique dans le monde. En France, selon l’Enquête Individuelle et Nationale sur les Consommations Alimentaires (INCA2 2006-2007), 38,9 % des hommes et 24,2 % des femmes adultes sont en surpoids et 11,6 % des adultes hommes et femmes sont obèses. En 2002, le coût médical de l’obésité est estimé entre 1,5 et 4,6 % des dépenses de santé selon l’Institut de recherche et documentation en économie de la santé (IRDES). Le développement de l’obésité et ses répercussions économiques ont conduit les pouvoirs publics à s’interroger sur les mesures susceptibles de modifier les comportements de consommation alimentaire. En estimant un système de demande, on évalue l’influence et la pertinence d’une politique de taxation des aliments à fortes teneurs en calories, en graisses et en sucres, dénommée « fat tax ». Nous montrons que les effets de cette « fat tax » sur les achats d’aliments et sur les achats de calories et de nutriments qui en résultent sont faibles. Son influence sur le poids des individus à court terme est également faible, mais tend à augmenter dans le long terme. Enfin, si la « fat tax » génère d'importantes recettes fiscales, elle affecte plus les ménages modestes.

Rebecca Zorach, Art for People’s Sake: Artists and Community in Black Chicago, 1965-1975

Dans cet ouvrage, l’historienne de l’art Rebecca Zorach compile ses travaux de recherche réalisés durant douze ans à la Northwestern University visant à réhabiliter les mouvements artistiques africains-américains investis au sein des communautés noires des quartiers populaires de Chicago de 1965 à 1975. Le Black Arts Movement étant souvent mieux connu pour ses activités en Californie ou à New York, l’auteure développe une historiographie précise qui révèle l’absence d’une littérature complète..

Ahmed & Rashid bin Shabib, Diaspora from the Middle East and North Africa: Communities, Architecture, Neighborhoods

Des Palestiniens tokyoïtes à la communauté irakienne de Londres en passant par les Iraniens établis à Los Angeles et la plus importante population assyrienne du monde en Suède, les jumeaux dubaïotes Ahmed et Rashid bin Shabib mettent en lumière dans cet ouvrage les communautés de la diaspora du Moyen Orient et d’Afrique du Nord à travers le monde. Entre reportages et récits de parcours individuels de personnalités telles que le sumotori égyptien Abdelrahman Shaalan ou la fille d’Edward Saïd, ..