43 citations · 64 across the 7 of their papers we have counts for
7 papers
Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science
Joseph Janssen, Shizhe Meng, Asad Haris +5
Scientists and statisticians often want to learn about the complex relationships that connect two time-varying variables. Recent work on sparse functional historical linear models…
A Targeted Approach to Confounder Selection for High-Dimensional Data
Asad Haris, Robert Platt
We consider the problem of selecting confounders for adjustment from a potentially large set of covariates, when estimating a causal effect. Recently, the high-dimensional Propensi…
On Data Enriched Logistic Regression
Cheng Zheng, Sayan Dasgupta, Yuxiang Xie +2
Biomedical researchers usually study the effects of certain exposures on disease risks among a well-defined population. To achieve this goal, the gold standard is to design a trial…
Generalized Sparse Additive Models
Asad Haris, Noah Simon, Ali Shojaie
We present a unified framework for estimation and analysis of generalized additive models in high dimensions. The framework defines a large class of penalized regression estimators…
Wavelet regression and additive models for irregularly spaced data
Asad Haris, Noah Simon, Ali Shojaie
We present a novel approach for nonparametric regression using wavelet basis functions. Our proposal, , can be applied to non-equispaced data with sample size no…
Nonparametric Regression with Adaptive Truncation via a Convex Hierarchical Penalty
Asad Haris, Ali Shojaie, Noah Simon
We consider the problem of non-parametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well-su…
Convex Modeling of Interactions with Strong Heredity
Asad Haris, Daniela Witten, Noah Simon
We consider the task of fitting a regression model involving interactions among a potentially large set of covariates, in which we wish to enforce strong heredity. We propose FAMIL…