论文标题
通用双线性模型的推断
Inference in generalized bilinear models
论文作者
论文摘要
潜在因子模型被广泛用于发现和调整现代应用中的隐藏变化。但是，大多数方法并未完全解释潜在因素的不确定性，这可能导致误解的推论，例如过度自信的p值。在本文中，我们开发了一种快速准确的不确定性定量方法，它是通用双线性模型的灵活扩展，它是广义线性模型的灵活扩展，以包括潜在因子以及行协变量，列协变量和相互作用。特别是，我们引入了Delta繁殖，这是一种使用Delta方法在模型组件之间传播不确定性的一般技术。此外，我们提供了一种快速收敛的算法，以最大程度地估计通过估计行和列分散体扩展早期方法的后部GBM估计。在模拟研究中，我们发现我们的方法提供了大多数感兴趣的参数的频繁覆盖范围。我们在癌症基因组学中的RNA-seq基因表达分析和拷贝比估计中进行了证明。
Latent factor models are widely used to discover and adjust for hidden variation in modern applications. However, most methods do not fully account for uncertainty in the latent factors, which can lead to miscalibrated inferences such as overconfident p-values. In this article, we develop a fast and accurate method of uncertainty quantification in generalized bilinear models, which are a flexible extension of generalized linear models to include latent factors as well as row covariates, column covariates, and interactions. In particular, we introduce delta propagation, a general technique for propagating uncertainty among model components using the delta method. Further, we provide a rapidly converging algorithm for maximum a posteriori GBM estimation that extends earlier methods by estimating row and column dispersions. In simulation studies, we find that our method provides approximately correct frequentist coverage of most parameters of interest. We demonstrate on RNA-seq gene expression analysis and copy ratio estimation in cancer genomics.
