Spike-and-slab priors model predictors as arising from a mixture of distributions: those that should (slab) or should not (spike) remain in the model. The spike-and-slab lasso (SSL) is a mixture of double exponentials, extending the single lasso penalty by imposing different penalties on parameters based on their inclusion probabilities. The SSL was extended to Generalized Linear Models (GLM) for application in genetics/genomics, and can handle many highly correlated predictors of a scalar outcome, but does not incorporate these relationships into variable selection. When images/spatial data are used to model a scalar outcome, relevant parameters tend to cluster spatially, and model performance may benefit from incorporating spatial structure into variable selection. We propose to incorporate spatial information by assigning intrinsic autoregressive priors to the logit prior probabilities of inclusion, which results in more similar shrinkage penalties among spatially adjacent parameters. Using MCMC to fit Bayesian models can be computationally prohibitive for large-scale data, but we fit the model by adapting a computationally efficient coordinate-descent-based EM algorithm. A simulation study and an application to Alzheimer’s Disease imaging data show that incorporating spatial information can improve model fitness.

Spike-and-slab 先验模型预测变量由分布的混合产生：那些应该（slab）或不应该（spike）保留在模型中。spike-and-slab lasso (SSL) 是双指数的混合，通过根据参数的包含概率对参数施加不同的惩罚来扩展单 lasso 惩罚。SSL 被扩展到广义线性模型 (GLM) 以应用于遗传学/基因组学，并且可以处理标量结果的许多高度相关的预测因子，但不会将这些关系纳入变量选择。当图像/空间数据用于对标量结果建模时，相关参数往往会在空间上聚集，并且模型性能可能会受益于将空间结构纳入变量选择。我们建议通过将内在自回归先验分配给包含的对数先验概率来合并空间信息，这会导致空间相邻参数之间更相似的收缩惩罚。使用 MCMC 来拟合贝叶斯模型对于大规模数据来说在计算上可能会让人望而却步，但我们通过采用计算效率高的基于坐标下降的 EM 算法来拟合模型。一项模拟研究和对阿尔茨海默病成像数据的应用表明，结合空间信息可以提高模型的适用性。但是我们通过采用计算效率高的基于坐标下降的 EM 算法来拟合模型。一项模拟研究和对阿尔茨海默病成像数据的应用表明，结合空间信息可以提高模型的适用性。但是我们通过采用计算效率高的基于坐标下降的 EM 算法来拟合模型。一项模拟研究和对阿尔茨海默病成像数据的应用表明，结合空间信息可以提高模型的适用性。