DoGR：用于异质数据可再现分析的非聚集高斯回归
DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data
摘要：
大规模数据的定量分析往往由于存在不同的子组而变得复杂，这降低了他们对保留数据进行推断的准确性。为了解决异构数据分析的挑战，我们引入了DoGR，这是一种通过同时将数据划分为重叠集群（分解）并对其中的行为进行建模（回归）来发现潜在混淆因素的方法。当应用于现实世界的数据时，我们的方法发现有意义的集群及其特征行为，从而深入了解群体差异及其对结果的影响。通过考虑潜在的混杂因素，我们的框架有助于对嘈杂的异构数据进行探索性分析，并可用于学习预测模型，更好地推广到新数据。我们提供的代码使其他人能够在其数据分析工作流程中使用DoGR。
英文摘要：
Quantitative analysis of large-scale data is often complicated by the presence of diverse subgroups, which reduce the accuracy of inferences they make on held-out data. To address the challenge of heterogeneous data analysis, we introduce DoGR, a method that discovers latent confounders by simultaneously partitioning the data into overlapping clusters (disaggregation) and modeling the behavior within them (regression). When applied to real-world data, our method discovers meaningful clusters and their characteristic behaviors, thus giving insight into group differences and their impact on the outcome of interest. By accounting for latent confounders, our framework facilitates exploratory analysis of noisy, heterogeneous data and can be used to learn predictive models that better generalize to new data. We provide the code to enable others to use DoGR within their data analytic workflows.
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