Incomplete generalized U-statistics for food risk assessment.
نویسندگان
چکیده
This article proposes statistical tools for quantitative evaluation of the risk due to the presence of some particular contaminants in food. We focus on the estimation of the probability of the exposure to exceed the so-called provisional tolerable weekly intake (PTWI), when both consumption data and contamination data are independently available. A Monte Carlo approximation of the plug-in estimator, which may be seen as an incomplete generalized U-statistic, is investigated. We obtain the asymptotic properties of this estimator and propose several confidence intervals, based on two estimators of the asymptotic variance: (i) a bootstrap type estimator and (ii) an approximate jackknife estimator relying on the Hoeffding decomposition of the original U-statistics. As an illustration, we present an evaluation of the exposure to Ochratoxin A in France.
منابع مشابه
Maximal Deviations of Incomplete U-statistics with Applications to Empirical Risk Sampling
It is the goal of this paper to extend the Empirical Risk Minimization (ERM) paradigm, from a practical perspective, to the situation where a natural estimate of the risk is of the form of a K-sample U -statistics, as it is the case in the K-partite ranking problem for instance. Indeed, the numerical computation of the empirical risk is hardly feasible if not infeasible, even for moderate sampl...
متن کاملUsing empirical likelihood to combine data: application to food risk assessment.
This article introduces an original methodology based on empirical likelihood, which aims at combining different food contamination and consumption surveys to provide risk managers with a risk measure, taking into account all the available information. This risk index is defined as the probability that exposure to a contaminant exceeds a safe dose. It is naturally expressed as a nonlinear funct...
متن کاملScaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics
In a wide range of statistical learning problems such as ranking, clustering or metric learning among others, the risk is accurately estimated by U-statistics of degree d ≥ 1, i.e. functionals of the training data with low variance that take the form of averages over k-tuples. From a computational perspective, the calculation of such statistics is highly expensive even for a moderate sample siz...
متن کاملEvaluation of cooking emitted particulate matter concentration and workers cancer risk assessment in the sari fast-food shops
Introduction: One of the harmful pollutants in the indoor environments is particulate matters. Particles smaller than 2.5 micrometer in diameter that are suspend in the industrial environments air are the most deleterious dusts which can cause lung disease and cancer. In present study PM2.5 concentration in the fast-food shops air and its cancer risk for shop workers were assessed. Material ...
متن کاملSGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk
In many learning problems, ranging from clustering to ranking through metric learning, empirical estimates of the risk functional consist of an average over tuples (e.g., pairs or triplets) of observations, rather than over individual observations. In this paper, we focus on how to best implement a stochastic approximation approach to solve such risk minimization problems. We argue that in the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Biometrics
دوره 62 1 شماره
صفحات -
تاریخ انتشار 2006