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Found 15 papers, 1 papers with code
Retire: Robust Expectile Regression in High Dimensions
In this paper, we propose and study (penalized) robust expectile regression (retire), with a focus on iteratively reweighted $\ell_1$-penalization which reduces the estimation bias from $\ell_1$-penalization and leads to oracle properties.
Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guarantees
We address the problem of how to achieve optimal inference in distributed quantile regression without stringent scaling conditions.
Smoothed Quantile Regression with Large-Scale Inference
Our numerical studies confirm the conquer estimator as a practical and reliable approach to large-scale inference for quantile regression.
Statistics Theory Methodology Statistics Theory
Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data
Motivated by functional magnetic resonance imaging (fMRI) studies, we propose a novel method for constructing brain connectivity networks with correlated replicates and latent effects.
Methodology
Model Linkage Selection for Cooperative Learning
The main crux is to sequentially incorporate additional learners that can enhance the prediction accuracy of an existing joint model based on user-specified parameter sharing patterns across a set of learners.
Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks
To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story.
Robust Sparse Reduced Rank Regression in High Dimensions
We propose robust sparse reduced rank regression for analyzing large and complex high-dimensional data with heavy-tailed random noise.
A convex formulation for high-dimensional sparse sliced inverse regression
Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of information on the conditional distribution of the response given the covariates.
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.
Graphical Nonconvex Optimization for Optimal Estimation in Gaussian Graphical Models
Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.
Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow
Sparse generalized eigenvalue problem (GEP) plays a pivotal role in a large family of high-dimensional statistical models, including sparse Fisher's discriminant analysis, canonical correlation analysis, and sufficient dimension reduction.
Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression
A major challenge for building statistical models in the big data era is that the available data volume far exceeds the computational capability.
Selection Bias Correction and Effect Size Estimation under Dependence
Many authors have proposed methods to reduce the effects of selection bias under the assumption that the naive estimates of the effect sizes are independent.
Learning Graphical Models With Hubs
We consider the problem of learning a high-dimensional graphical model in which certain hub nodes are highly-connected to many other nodes.
The Cluster Graphical Lasso for improved estimation of Gaussian graphical models
We begin by introducing a surprising connection between the graphical lasso and hierarchical clustering: the graphical lasso in effect performs a two-step procedure, in which (1) single linkage hierarchical clustering is performed on the variables in order to identify connected components, and then (2) an l1-penalized log likelihood is maximized on the subset of variables within each connected component.
