21 citations · 58 across the 36 of their papers we have counts for
9 papers · 1 filter
Quantile Regression by Dyadic CART
Oscar Hernan Madrid Padilla, Sabyasachi Chatterjee
In this paper we propose and study a version of the Dyadic Classification and Regression Trees (DCART) estimator from Donoho (1997) for (fixed design) quantile regression in genera…
Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients
Fan Wang, Oscar Hernan Madrid Padilla, Yi Yu +1
We study the theoretical properties of the fused lasso procedure originally proposed by \cite{tibshirani2005sparsity} in the context of a linear regression model in which the regre…
Optimal partition recovery in general graphs
Yi Yu, Oscar Hernan Madrid Padilla, Alessandro Rinaldo
We consider a graph-structured change point problem in which we observe a random vector with piecewise constant but unknown mean and whose independent, sub-Gaussian coordinates cor…
2D score based estimation of heterogeneous treatment effects
Steven Siwei Ye, Yanzhen Chen, Oscar Hernan Madrid Padilla
Statisticians show growing interest in estimating and analyzing heterogeneity in causal effects in observational studies. However, there usually exists a trade-off between accuracy…
A causal fused lasso for interpretable heterogeneous treatment effects estimation
Oscar Hernan Madrid Padilla, Yanzhen Chen, Carlos Misael Madrid Padilla +1
We propose a novel method for estimating heterogeneous treatment effects based on the fused lasso. By first ordering samples based on the propensity or prognostic score, we match u…
Scalable Bayesian change point detection with spike and slab priors
Lorenzo Cappello, Oscar Hernan Madrid Padilla, Julia A. Palacios
We study the use of spike and slab priors for consistent estimation of the number of change points and their locations. Leveraging recent results in the variable selection literatu…