A generative approach to modeling data with quantitative and qualitative responses
A generative approach to modeling data with quantitative and qualitative responses
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DOI：
10.1016/j.jmva.2022.104952
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Xiaoning Kang;Lulu Kang;Wei Chen;Xinwei Deng
中科院分区：
文献类型：
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作者：
Xiaoning Kang;Lulu Kang;Wei Chen;Xinwei Deng
在许多科学领域，经常会遇到具有大量预测变量的混合定量和定性 (QQ) 响应的数据。通过探索 QQ 响应之间的关联，现有方法通常考虑给定预测变量的 QQ 响应的联合模型。然而，预测变量之间的依赖性也为拟合 QQ 响应提供了有用的信息。因此，在这项工作中，我们提出了一种新颖的生成方法，通过结合预测变量的依赖信息来联合建模 QQ 响应。所提出的方法计算效率高，并且在惩罚似然框架下提供准确的参数估计。此外，由于生成方法框架，该方法的渐近理论结果是在某些规律性条件下成立的。通过材料科学和遗传学领域的模拟和真实案例研究来检验所提出方法的性能。
In many scientific areas, data with mixed quantitative and qualitative (QQ) responses are commonly encountered with a large number of predictors. By exploring the association between QQ responses, existing approaches often consider a joint model of QQ responses given the predictor variables. However, the dependency among predictive variables also provides useful information for fitting QQ responses. Hence in this work, we propose a novel generative approach to jointly model the QQ responses by incorporating the dependency information of predictors. The proposed method is computationally efficient and provides accurate parameter estimation under a penalized likelihood framework. Moreover, because of the generative approach framework, the asymptotically theoretical results of the proposed method are established under some regularity conditions. The performance of the proposed method is examined through simulations and real case studies in material science and genetics.