Purpose

The purpose of this paper is to bring out the development of a flexible model for nonstationary crop yield distributions and its applications to decision-making in crop insurance.

Design/methodology/approach

The authors design a nonparametric Bayesian approach based on Gaussian process regressions to model crop yields over time. Further flexibility is obtained via Bayesian model averaging that results in mixed Gaussian processes.

Findings

Simulation results on crop insurance premium rates show that the proposed method compares favorably with conventional estimators, especially when the underlying distributions are nonstationary.

Originality/value

Unlike conventional two-stage estimation, the proposed method models nonstationary crop yields in a single stage. The authors further adopt a decision theoretic framework in its empirical application and demonstrate that insurance companies can use the proposed method to effectively identify profitable policies under symmetric or asymmetric loss functions.

中文翻译：

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非平稳作物产量分布的高斯过程建模与作物保险的应用

目的

本文的目的是为非平稳作物产量分布开发一个灵活的模型及其在作物保险决策中的应用。

设计/方法/方法

作者设计了一种基于高斯过程回归的非参数贝叶斯方法来模拟作物产量随时间的变化。进一步的灵活性是通过贝叶斯模型平均获得的，这导致混合高斯过程。

发现

作物保险费率的模拟结果表明，所提出的方法与传统的估计方法相比具有优势，特别是当基础分布是非平稳的时。

原创性/价值

与传统的两阶段估计不同，所提出的方法对单个阶段的非平稳作物产量进行建模。作者在其实证应用中进一步采用了决策理论框架，并证明保险公司可以使用所提出的方法有效识别对称或非对称损失函数下的盈利保单。