论文标题
Spike and Slab遇到Lasso：Spike and Slab Lasso的评论
Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO
论文作者
论文摘要
在过去的几十年中，高维数据集已变得无处不在，通常比观察值更多的协变量。在频繁的环境中，受惩罚的可能性方法是在高维数据中选择和估计的最流行方法。在贝叶斯框架中，尖峰和锯齿方法通常用作高维建模的概率构造。在线性回归的背景下，Rockova和George（2018）引入了Spike and-Slab Lasso（SSL），这是一种基于先验的方法，在该方法中提供了惩罚的可能性Lasso和Bayesian Point-Mass Spike-spike-and-Slab配方之间的连续体。自成立以来，Spike and-Slab拉索已扩展到各种环境，包括广义线性模型，因子分析，图形模型和非参数回归。本文的目的是调查周围尖峰和斜肌拉索方法的景观。首先，我们阐明了SSL先验在高维度中的有吸引力的特性和计算障碍。然后，我们回顾了SSL的方法论发展，并概述了几个理论发展。我们说明了模拟和真实数据集的方法。
High-dimensional data sets have become ubiquitous in the past few decades, often with many more covariates than observations. In the frequentist setting, penalized likelihood methods are the most popular approach for variable selection and estimation in high-dimensional data. In the Bayesian framework, spike-and-slab methods are commonly used as probabilistic constructs for high-dimensional modeling. Within the context of linear regression, Rockova and George (2018) introduced the spike-and-slab LASSO (SSL), an approach based on a prior which provides a continuum between the penalized likelihood LASSO and the Bayesian point-mass spike-and-slab formulations. Since its inception, the spike-and-slab LASSO has been extended to a variety of contexts, including generalized linear models, factor analysis, graphical models, and nonparametric regression. The goal of this paper is to survey the landscape surrounding spike-and-slab LASSO methodology. First we elucidate the attractive properties and the computational tractability of SSL priors in high dimensions. We then review methodological developments of the SSL and outline several theoretical developments. We illustrate the methodology on both simulated and real datasets.
