On Some Optimal Bayesian Nonparametric Rules for Estimating Distribution Functions
结合贝叶斯非参数推断、决策理论和稳健性，提出一种估计分布函数的新方法。在二次损失下，基于狄利克雷过程样本，按极小化极大和遗憾准则得到最优估计，并用模拟数据和城市管网燃气泄漏数据比较了不同估计的效果。
In this paper, we present a novel approach to estimating distribution functions, which combines ideas from Bayesian nonparametric inference, decision theory and robustness. Given a sample from a Dirichlet process on the space (𝒳, A), with parameter η in a class of measures, the sampling distribution function is estimated according to some optimality criteria (mainly minimax and regret), when a quadratic loss function is assumed. Estimates are then compared in two examples: one with simulated data and one with gas escapes data in a city network.