ASSESSING AQUATIC TOXICITY ASSESSMENT VIA A CLUSTERED VARIANCE MODEL

• 成果类型: Article
• 署名作者: Wang, Xin; Zhang, Jing
• 署名单位: California State University System; San Diego State University; University System of Ohio; Miami University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1884
• 发表日期: 2024
• 页码: 2342-2358
• 关键词: VARIABLE SELECTION

摘要

Motivated by the need to assess consistency in the outcomes of aquatic toxicity tests conducted by different labs at different time points, we propose a clustering of variance method in linear mixed models. The proposed method, referred as CVM, is able to identify the cluster structure of the variances and estimate model parameters simultaneously. In our proposed method, a penalized approach based on pairwise penalties is proposed to identify the cluster structure. We construct an optimization problem and develop an algorithm based on the alternating direction method of multipliers. Simulation studies show that the proposed approach can identify the cluster structure well and outperforms traditional methods based on k-means. In the end, the proposed approach is applied to the aquatic toxicity assessment data, which gives a more reasonable cluster structure than the traditional methods.