Type I Tobit Bayesian Additive Regression Trees for censored outcome regression

IF 1.6 2区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Statistics and Computing Pub Date : 2024-05-24 DOI:10.1007/s11222-024-10434-4

Eoghan O’Neill

引用次数: 0

Abstract

Censoring occurs when an outcome is unobserved beyond some threshold value. Methods that do not account for censoring produce biased predictions of the unobserved outcome. This paper introduces Type I Tobit Bayesian Additive Regression Tree (TOBART-1) models for censored outcomes. Simulation results and real data applications demonstrate that TOBART-1 produces accurate predictions of censored outcomes. TOBART-1 provides posterior intervals for the conditional expectation and other quantities of interest. The error term distribution can have a large impact on the expectation of the censored outcome. Therefore, the error is flexibly modeled as a Dirichlet process mixture of normal distributions. An R package is available at https://github.com/EoghanONeill/TobitBART.

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用于删减结果回归的 I 类托比特贝叶斯加法回归树

当一个结果的观测值超过某个临界值时，就会发生删减。不考虑删减的方法对未观察结果的预测会产生偏差。本文介绍了针对剔除结果的 I 类托比特贝叶斯加性回归树（TOBART-1）模型。模拟结果和实际数据应用证明，TOBART-1 能准确预测剔除结果。TOBART-1 提供了条件期望和其他相关量的后验区间。误差项的分布会对删减结果的期望值产生很大影响。因此，误差被灵活地建模为正态分布的 Dirichlet 过程混合物。R 软件包见 https://github.com/EoghanONeill/TobitBART。

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来源期刊

Statistics and Computing 数学-计算机：理论方法

CiteScore

3.20

自引率

4.50%

发文量

审稿时长

6-12 weeks

期刊介绍： Statistics and Computing is a bi-monthly refereed journal which publishes papers covering the range of the interface between the statistical and computing sciences. In particular, it addresses the use of statistical concepts in computing science, for example in machine learning, computer vision and data analytics, as well as the use of computers in data modelling, prediction and analysis. Specific topics which are covered include: techniques for evaluating analytically intractable problems such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification. In addition, the journal contains original research reports, authoritative review papers, discussed papers, and occasional special issues on particular topics or carrying proceedings of relevant conferences. Statistics and Computing also publishes book review and software review sections.

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