Nonparametric Predictive Inference for Exposure Assessment

被引：1

作者：

Roelofs, V. J. ^{[1
]}

Coolen, F. P. A. ^{[1
]}

Hart, A. D. M. ^{[1
]}

机构：

[1] Food & Environm Res Agcy, York YO41 1LZ, N Yorkshire, England

来源：

RISK ANALYSIS | 2011年 / 31卷 / 02期

基金：

英国工程与自然科学研究理事会;

关键词：

Bayesian methods; exposure assessment; M functions; nonparametric predictive inference;

D O I：

10.1111/j.1539-6924.2010.01490.x

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

Exposure assessment for food and drink consumption requires the combining of information about people's consumption of products with concentration data sets to provide predictions for chemical intake by humans. In this article, we present a method called nonparametric predictive inference (NPI) for exposure assessment. NPI is a distribution-free method relying only on Hill's assumption A((n)). Effectively, A((n)) is a postdata exchangeability assumption, which is a natural starting point for nonparametric statistics. For further discussion we refer to works by Hill and Coolen. We illustrate how NPI can be implemented to produce predictions for an individual's exposure based on consumption, body weight, and concentration data. NPI has the advantage that we do not have to assume a distribution to implement it. There may, however, be information available to suggest a distribution for a random quantity. Therefore, we present an NPI-Bayes hybrid method where this information can be taken into account by using Bayesian methods while using NPI for the other random quantities in the model.

引用

页码：218 / 227

页数：10

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