Bayesian Double-Penalty Tobit Quantile Regression for Longitudinal Interval-Censored Data

Shikhar Tyagi, Arvind Pandey, Bhupendra Singh, Vrijesh Tripathi

1. Overview

The BDPTobitQR package implements the Bayesian Double-Penalty Tobit Quantile Regression methods for longitudinal interval-censored data as proposed by Zhao, Shu, Hu, & Luo (2024) (Mathematics, 12(12), 1782).

2. Methods

• PDAL-BTQR: Double Adaptive Lasso penalty for fixed and random effects.
• PDL-BTQR: Double Lasso penalty for fixed and random effects.
• P-BTQR: Unpenalized Tobit quantile regression with mixed effects.

3. Usage Example

library(BDPTobitQR)

# Simulate longitudinal interval-censored data
dat <- sim_longitudinal_data(n = 15, m = 4, p = 4, seed = 123)

# Fit model
fit <- bdp_tobit_qr(
  formula = y ~ x1 + x2 + x3 + x4,
  random = ~ 1,
  data = dat,
  id = dat$id,
  lower = dat$lower,
  upper = dat$upper,
  tau = 0.5,
  method = "PDAL-BTQR"
)

summary(fit)
plot(fit)

4. References

Zhao, K., Shu, T., Hu, C., & Luo, Y. (2024). Research on Quantile Regression Method for Longitudinal Interval-Censored Data Based on Bayesian Double Penalty. Mathematics, 12(12), 1782.