11 citations · 45 across the 14 of their papers we have counts for
14 papers
Estimation and Hypothesis Testing of Derivatives in Smoothing Spline ANOVA Models
Ruiqi Liu, Kexuan Li, Meng Li
Within the framework of smoothing spline ANOVA, we propose a plug-in kernel ridge regression estimator to estimate the derivatives of the underlying multivariate regression functio…
Statistical Inference with Stochastic Gradient Methods under -mixing Data
Ruiqi Liu, Xi Chen, Zuofeng Shang
Stochastic gradient descent (SGD) is a scalable and memory-efficient optimization algorithm for large datasets and stream data, which has drawn a great deal of attention and popula…
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary
Tianyang Hu, Ruiqi Liu, Zuofeng Shang +1
Deep learning has gained huge empirical successes in large-scale classification problems. In contrast, there is a lack of statistical understanding about deep learning methods, par…
Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks
Kexuan Li, Fangfang Wang, Lingli Yang +1
The applications of traditional statistical feature selection methods to high-dimension, low sample-size data often struggle and encounter challenging problems, such as overfitting…
Calibrating multi-dimensional complex ODE from noisy data via deep neural networks
Kexuan Li, Fangfang Wang, Ruiqi Liu +2
Ordinary differential equations (ODEs) are widely used to model complex dynamics that arises in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated…
Online Statistical Inference for Parameters Estimation with Linear-Equality Constraints
Ruiqi Liu, Mingao Yuan, Zuofeng Shang
Stochastic gradient descent (SGD) and projected stochastic gradient descent (PSGD) are scalable algorithms to compute model parameters in unconstrained and constrained optimization…
Distributed Adaptive Nearest Neighbor Classifier: Algorithm and Theory
Ruiqi Liu, Ganggang Xu, Zuofeng Shang
When data is of an extraordinarily large size or physically stored in different locations, the distributed nearest neighbor (NN) classifier is an attractive tool for classification…
A Computationally Efficient Classification Algorithm in Posterior Drift Model: Phase Transition and Minimax Adaptivity
Ruiqi Liu, Kexuan Li, Zuofeng Shang
In massive data analysis, training and testing data often come from very different sources, and their probability distributions are not necessarily identical. A feature example is…
On Deep Instrumental Variables Estimate
Ruiqi Liu, Zuofeng Shang, Guang Cheng
The endogeneity issue is fundamentally important as many empirical applications may suffer from the omission of explanatory variables, measurement error, or simultaneous causality.…
Statistical Inference on Partially Linear Panel Model under Unobserved Linearity
Ruiqi Liu, Ben Boukai, Zuofeng Shang
A new statistical procedure, based on a modified spline basis, is proposed to identify the linear components in the panel data model with fixed effects. Under some mild assumptions…
Optimal Nonparametric Inference via Deep Neural Network
Ruiqi Liu, Ben Boukai, Zuofeng Shang
Deep neural network is a state-of-art method in modern science and technology. Much statistical literature have been devoted to understanding its performance in nonparametric estim…
Nonparametric Inference under B-bits Quantization
Kexuan Li, Ruiqi Liu, Ganggang Xu +1
Statistical inference based on lossy or incomplete samples is often needed in research areas such as signal/image processing, medical image storage, remote sensing, signal transmis…
Testing Community Structures for Hypergraphs
Mingao Yuan, Ruiqi Liu, Yang Feng +1
Many complex networks in real world can be formulated as hypergraphs where community detection has been widely used. However, the fundamental question of whether communities exist…
Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method
Shunan Zhao, Ruiqi Liu, Zuofeng Shang
We propose statistical inferential procedures for panel data models with interactive fixed effects in a kernel ridge regression framework.Compared with traditional sieve methods, o…