Abstract

In existing pH acceleration models, which are used to assess the shelf-life of liquid-phase nano-sol products, a mixture of normal distributions is commonly employed to describe the sizes of particles mixing from two populations. The Gaussian mixture model approach falls short when used to characterize the asymmetric distributions of particle sizes in subgroups. This work considers instead a broader class of a mixture of log-F distributions, to be embedded in a pH acceleration model. This study aims at understanding the impact of the new modeling approach, in the presence of model misspecification of the particle size distribution, on the accuracy and precision for making the shelf-life predictions. This study found that model misspecification indeed significantly affects the shelf-life predictions. The proposed method shows favorable finite sample performance on a simulated data set. Both the quantitative analysis of the impact due to model misspecification and the solution proposed herein could benefit practitioners in the long run.

摘要

在现有的用于评估液相纳米溶胶产品保质期的 pH 加速模型中，通常使用正态分布的混合物来描述两个群体混合的颗粒大小。高斯混合模型方法在用于表征子组中粒度的不对称分布时不足。这项工作考虑了更广泛的 log-F 分布混合物类别，嵌入到 pH 加速模型中。本研究旨在了解新建模方法的影响，在存在模型错误指定粒度分布的情况下，对保质期预测的准确性和精度的影响。这项研究发现，模型错误指定确实会显着影响保质期预测。所提出的方法在模拟数据集上显示出良好的有限样本性能。从长远来看，对模型错误指定造成的影响的定量分析和本文提出的解决方案都可以使从业者受益。