Normalized random measures driven by increasing additive processes

Bayesian quantile additive regression trees

Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors. However, quantile regression trees and their ensembles have not yet garnered much attention despite the increasing popularity of the linear quantile regression model. This work proposes a Bayesian quantile additive regression trees model that shows very good predictive performance illustrated using simulation studies and real data applications. Further extension to tackle binary classification problems is also considered.

关于贝叶斯加法回归树计算效率的分析：渐近分析

On the Computational Efficiency of Bayesian Additive Regression Trees: An Asymptotic Analysis

本文分析了BART采样器在大规模数据下的计算效率，发现其收敛时间随样本量增加而增加，但通过增加树数量或提高温度可有效缓解这一问题。

Particle Gibbs for Bayesian Additive Regression Trees

Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, where there is also demand for probabilistic predictions with measures of uncertainty, the Bayesian additive regression trees (BART) model, introduced by Chipman et al. (2010), is increasingly popular. As data sets have grown in size, however, the standard Metropolis-Hastings algorithms used to perform inference in BART are proving inadequate. In particular, these Markov chains make local changes to the trees and suff…

贝叶斯惩罚变换模型：任意条件分布的结构化加性位置-尺度回归

Bayesian Penalized Transformation Models: Structured Additive Location-Scale Regression for Arbitrary Conditional Distributions

提出贝叶斯惩罚变换模型，通过半参数位置-尺度回归直接估计响应变量的条件分布，结合结构化加性预测器和光滑先验，实现不确定量化，并在模拟和实际数据中验证有效性。

粒子系统加法函数的渐进行为 via 斯坦方法

Asymptotics for additive functionals of particle systems via Stein's method

本文通过斯坦方法与梅克尔公式，建立了粒子系统加法函数的渐进行为理论，首次给出了移动测度模型在Wasserstein距离上的定量收敛速率。

Estimation and Model Identification of Locally Stationary Varying-Coefficient Additive Models

Nonparametric regression models with locally stationary covariates have received increasing interest in recent years. As a nice relief of "curse of dimensionality" induced by large dimension of covariates, additive regression model is commonly used. However, in locally stationary context, to catch the dynamic nature of regression function, we adopt a flexible varying-coefficient additive model where the regression function has the form $α_{0}\left(u\right)+\sum_{k=1}^{p}α_{k}\left(u\right)β_{k}\left(x_{k}\right).$ For this model, we propose a three-step spline estimation method for each univar…