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Auteur principal: O'Neill, Eoghan
Format: Preprint
Publié: 2022
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Accès en ligne:https://arxiv.org/abs/2211.07506
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author O'Neill, Eoghan
author_facet O'Neill, Eoghan
contents 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.
format Preprint
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publishDate 2022
record_format arxiv
spellingShingle Type I Tobit Bayesian Additive Regression Trees for Censored Outcome Regression
O'Neill, Eoghan
Econometrics
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.
title Type I Tobit Bayesian Additive Regression Trees for Censored Outcome Regression
topic Econometrics
url https://arxiv.org/abs/2211.07506