Estimation of Mixture Models using Cross‐Validation Optimization: Implications for Crop Yield Distribution Modeling

Abstract

A critical issue in identifying an appropriate characterization of crop yield distributions is that the best‐fitting distribution in an in‐sample framework is not necessarily the best choice out‐of‐sample. This study provides a methodology for estimating flexible and efficient mixture models using cross‐validation that alleviates many of these associated model selection issues. The method is illustrated in an application to the rating of group risk insurance products. Results indicate that nonparametric models often fit best in‐sample but are inefficient and consistently overstate true rates, and vice versa for parametric models. The proposed model provides unbiased rates and also has desirable efficiency properties.