论文标题
快速贝叶斯推断非参数添加剂位置尺度模型，具有右和间隔的数据
Fast Bayesian Inference in Nonparametric Double Additive Location-Scale Models With Right- and Interval-Censored Data
论文作者
论文摘要
惩罚的b-splines通常在加性模型中使用，以描述用定量协变量的响应中的平滑变化。通常，使用广义添加剂模型在指数家族中的条件平均值来完成，并间接影响其他条件矩。另一个共同的策略在于集中在几个低阶条件矩上，使完整的条件分布未指定。或者，可以假定多参数分布使用添加剂表达式在协变量上共同回归的几个参数。 我们的工作可以连接到后者的建议，以进行右或间隔审查的连续响应，并具有高度灵活且光滑的非参数密度。我们专注于有条件平均值和标准偏差中具有添加术语的位置尺度模型。从贝叶斯框架的最新结果开始，我们提出了一种快速融合算法，以从其边际后代选择惩罚参数。它依靠拉普拉斯近似与样条参数的条件后部。模拟表明，与工作高斯假设的方法相比，所谓的估计量具有出色的频繁特性并提高效率。我们通过分析不符合测量的收入数据来说明方法。
Penalized B-splines are routinely used in additive models to describe smooth changes in a response with quantitative covariates. It is typically done through the conditional mean in the exponential family using generalized additive models with an indirect impact on other conditional moments. Another common strategy consists in focussing on several low-order conditional moments, leaving the complete conditional distribution unspecified. Alternatively, a multi-parameter distribution could be assumed for the response with several of its parameters jointly regressed on covariates using additive expressions. Our work can be connected to the latter proposal for a right- or interval-censored continuous response with a highly flexible and smooth nonparametric density. We focus on location-scale models with additive terms in the conditional mean and standard deviation. Starting from recent results in the Bayesian framework, we propose a quickly converging algorithm to select penalty parameters from their marginal posteriors. It relies on Laplace approximations to the conditional posterior of the spline parameters. Simulations suggest that the so-obtained estimators own excellent frequentist properties and increase efficiency as compared to approaches with a working Gaussian hypothesis. We illustrate the methodology with the analysis of imprecisely measured income data.
