Search
Search Results
-
Variable Selection for High-dimensional Cox Model with Error Rate Control
Simultaneously finding active predictors and controlling the false discovery rate (FDR) for high-dimensional survival data is an important but...
-
Ultra-High Dimensional Model Averaging for Multi-Categorical Response
Model averaging has been considered to be a powerful tool for model-based prediction in the past decades. However, its application in ultra-high...
-
High-dimensional density estimation with tensorizing flow
We propose the tensorizing flow method for estimating high-dimensional probability density functions from observed data. Our method combines the...
-
Discretization and index-robust error analysis for constrained high-index saddle dynamics on the high-dimensional sphere
We develop and analyze numerical discretization to the constrained high-index saddle dynamics, the dynamics searching for the high-index saddle...
-
Variable transformations in combination with wavelets and ANOVA for high-dimensional approximation
We use hyperbolic wavelet regression for the fast reconstruction of high-dimensional functions having only low-dimensional variable interactions....
-
High-dimensional approximation with kernel-based multilevel methods on sparse grids
Moderately high-dimensional approximation problems can successfully be solved by combining univariate approximation processes using an intelligent...
-
Selection of Fixed Effects in High-dimensional Generalized Linear Mixed Models
The selection of fixed effects is studied in high-dimensional generalized linear mixed models (HDGLMMs) without parametric distributional assumptions...
-
Three-Dimensional Simulation of a High-Velocity Body Motion in a Tube with Rarefied Gas
AbstractFlow around a body moving at a high subsonic velocity in a tube filled with rarefied gas is studied. This aerodynamic problem is considered...
-
A Factor-GARCH Model for High Dimensional Volatilities
This paper proposes a method for modelling volatilities (conditional covariance matrices) of high dimensional dynamic data. We combine the ideas of...
-
Multi-layer Bundling as a New Approach for Determining Multi-scale Correlations Within a High-Dimensional Dataset
The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and...
-
VI-DGP: A Variational Inference Method with Deep Generative Prior for Solving High-Dimensional Inverse Problems
Solving high-dimensional Bayesian inverse problems (BIPs) with the variational inference (VI) method is promising but still challenging. The main...
-
Two-Stage Online Debiased Lasso Estimation and Inference for High-Dimensional Quantile Regression with Streaming Data
In this paper, the authors propose a two-stage online debiased lasso estimation and statistical inference method for high-dimensional quantile...
-
A deep learning method for pricing high-dimensional American-style options via state-space partition
This paper proposes a deep learning approach for solving optimal stop** problems and high-dimensional American-style options pricing problems....
-
Feature Selection for High-Dimensional Varying Coefficient Models via Ordinary Least Squares Projection
Feature selection is a changing issue for varying coefficient models when the dimensionality of covariates is ultrahigh. The traditional technology...
-
Connecting Agent-Based Models with High-Dimensional Parameter Spaces to Multidimensional Data Using SMoRe ParS: A Surrogate Modeling Approach
Across a broad range of disciplines, agent-based models (ABMs) are increasingly utilized for replicating, predicting, and understanding complex...
-
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression
Federated learning has become a popular tool in the big data era nowadays. It trains a centralized model based on data from different clients while...
-
Volume Properties of High-Dimensional Orlicz Balls
We prove asymptotic estimates for the volume of families of Orlicz balls in high dimensions. As an application, we describe a large family of Orlicz...
-
An Adaptive ANOVA Stochastic Galerkin Method for Partial Differential Equations with High-dimensional Random Inputs
It is known that standard stochastic Galerkin methods encounter challenges when solving partial differential equations with high-dimensional random...
-
Bifurcation features, chaos, and coherent structures for one-dimensional nonlinear electrical transmission line
This paper intends to investigate the bifurcation features with chaos and nonlinear coherent structures for the voltage wave propagation by...
-
Differentially private SGD with random features
In the realm of large-scale machine learning, it is crucial to explore methods for reducing computational complexity and memory demands while...