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Risk-Based Sampling: I Don't Want to Weight in Vain

机译：基于风险的抽样：我不想白费力气

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Recently, there has been considerable interest in developing risk-based sampling for food safety and animal and plant health for efficient allocation of inspection and surveillance resources. The problem of risk-based sampling allocation presents a challenge similar to financial portfolio analysis. Markowitz (1952) laid the foundation for modern portfolio theory based on mean-variance optimization. However, a persistent challenge in implementing portfolio optimization is the problem of estimation error, leading to false "optimal" portfolios and unstable asset weights. In some cases, portfolio diversification based on simple heuristics (e.g., equal allocation) has better out-of-sample performance than complex portfolio optimization methods due to estimation uncertainty. Even for portfolios with a modest number of assets, the estimation window required for true optimization may imply an implausibly long stationary period. The implications for risk-based sampling are illustrated by a simple simulation model of lot inspection for a small, heterogeneous group of producers.
机译：最近，人们对开发基于风险的食品安全和动植物健康抽样以有效分配检查和监视资源引起了极大兴趣。基于风险的抽样分配问题提出了与金融投资组合分析类似的挑战。 Markowitz（1952）为基于均值方差优化的现代投资组合理论奠定了基础。但是，在实现投资组合优化方面，一个持续的挑战是估计误差问题，导致错误的“最优”投资组合和不稳定的资产权重。在某些情况下，由于估算的不确定性，基于简单试探法（例如，均等分配）的投资组合多元化比复杂的投资组合优化方法具有更好的样本外表现。即使对于资产数量适中的投资组合，真正优化所需的估计窗口也可能意味着难以置信的长时间停滞期。基于风险的抽样的含义可以通过一个简单的批量检验模拟模型来说明，该模型用于一小群异类生产者。

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