arXiv 14 Nov 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2211.07506 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by appendix_command · 68% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | George, E., P. Laud, B. Logan, R. McCulloch, and R. Sparapani (2019) Fully nonparametric bayesian additive regression trees, Topics in Identification, Limited Dependent Variables, Partial Observabi… | 0.941 | 6 | 3 | 83% |
| 2 | Linero, A.R. and Y. Yang (2018) Bayesian regression tree ensembles that adapt to smoothness and sparsity | 0.928 | 4 | 3 | 100% |
| 3 | Chipman, H.A., E.I. George, and R.E. McCulloch (2010) Bart: Bayesian additive regression trees | 0.920 | 9 | 4 | 78% |
| 4 | Sigrist, F. and C. Hirnschall (2019) Grabit: Gradient tree-boosted tobit models for default prediction | 0.863 | 28 | 4 | 64% |
| 5 | Jacobson, T. and H. Zou (2022) High-dimensional censored regression via the penalized tobit likelihood | 0.857 | 27 | 4 | 63% |
| 6 | Groot, P. and P.J. Lucas (2012) Gaussian process regression with censored data using expectation propagation | 0.847 | 28 | 4 | 61% |
| 7 | Friedman, J.H (1991) Multivariate adaptive regression splines | 0.831 | 47 | 3 | 57% |
| 8 | Chib, S (1992) Bayes inference in the tobit censored regression model | 0.737 | 3 | 2 | 100% |
| 9 | Nie, X. and S. Wager (2021) Quasi-oracle estimation of heterogeneous treatment effects | 0.693 | 7 | 1 | 100% |
| 10 | Friedberg, R., J. Tibshirani, S. Athey, and S. Wager (2020) Local linear forests | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 62 scored citations.