Fusion Learning of Functional Linear Regression with Application to Genotype-by-Environment Interaction Studies
Fusion Learning of Functional Linear Regression with Application to Genotype-by-Environment Interaction Studies
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DOI：
10.1007/s13253-023-00529-2
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Shan Yu;Aaron Kusmec;Li Wang;D. Nettleton
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文献类型：
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作者：
Shan Yu;Aaron Kusmec;Li Wang;D. Nettleton
我们提出了一种稀疏多组函数线性回归模型来同时估计多个系数函数并识别组，使得系数函数在组内相同且在组间不同。通过借用相关受试者亚组的信息，我们的方法提高了估计效率，同时保留了模型参数和系数函数的异质性。我们使用自适应融合套索惩罚将系数估计缩小到每组内的公共值。我们还建立了所提出的估计量的理论属性。为了提高计算效率并合并邻域信息，我们建议使用图约束自适应套索和计算高效的算法。已经进行了两项蒙特卡罗模拟研究来研究所提出方法的有限样本性能。该方法适用于 Genomes to Fields 联盟的高粱开花时间数据和杂交玉米产量。本文附带的补充材料出现在网上。
We propose a sparse multi-group functional linear regression model to simultaneously estimate multiple coefficient functions and identify groups, such that coefficient functions are identical within groups and distinct across groups. By borrowing information from relevant subgroups of subjects, our method enhances estimation efficiency while preserving heterogeneity in model parameters and coefficient functions. We use an adaptive fused lasso penalty to shrink coefficient estimates to a common value within each group. We also establish theoretical properties of the proposed estimators. To enhance computation efficiency and incorporate neighborhood information, we propose to use graph-constrained adaptive lasso with a computationally efficient algorithm. Two Monte Carlo simulation studies have been conducted to study the finite-sample performance of the proposed method. The proposed method is applied to sorghum flowering-time data and hybrid maize grain yields from the Genomes to Fields consortium. Supplementary materials accompanying this paper appear online.