144 citations · 302 across the 14 of their papers we have counts for
14 papers
Conditional Testing based on Localized Conformal p-values
Xiaoyang Wu, Lin Lu, Zhaojun Wang +1
In this paper, we address conditional testing problems through the conformal inference framework. We define the localized conformal p-values by inverting prediction intervals and p…
Leverage classifier: Another look at support vector machine
Yixin Han, Jun Yu, Nan Zhang +4
Support vector machine (SVM) is a popular classifier known for accuracy, flexibility, and robustness. However, its intensive computation has hindered its application to large-scale…
Reliever: Relieving the Burden of Costly Model Fits for Changepoint Detection
Chengde Qian, Guanghui Wang, Changliang Zou
Changepoint detection typically relies on a grid-search strategy for optimal data segmentation. When model fitting itself is expensive, repeatedly fitting a model on every candidat…
Zipper: Addressing degeneracy in algorithm-agnostic inference
Geng Chen, Yinxu Jia, Guanghui Wang +1
The widespread use of black box prediction methods has sparked an increasing interest in algorithm/model-agnostic approaches for quantifying goodness-of-fit, with direct ties to sp…
Statistical Inference for Ultrahigh Dimensional Location Parameter Based on Spatial Median
Guanghui Cheng, Liuhua Peng, Changliang Zou
Motivated by the widely used geometric median-of-means estimator in machine learning, this paper studies statistical inference for ultrahigh dimensionality location parameter based…
Selective conformal inference with false coverage-statement rate control
Yajie Bao, Yuyang Huo, Haojie Ren +1
Conformal inference is a popular tool for constructing prediction intervals (PI). We consider here the scenario of post-selection/selective conformal inference, that is PIs are rep…
Model-free controlled variable selection via data splitting
Yixin Han, Xu Guo, Changliang Zou
Addressing the simultaneous identification of contributory variables while controlling the false discovery rate (FDR) in high-dimensional data is a crucial statistical challenge. I…
Model-Free Statistical Inference on High-Dimensional Data
Xu Guo, Runze Li, Zhe Zhang +1
This paper aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduc…
Multi-Kink Quantile Regression for Longitudinal Data with Application to the Progesterone Data Analysis
Chuang Wan, Wei Zhong, Wenyang Zhang +1
Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multi-kink quantile regression model for lo…
Dynamic statistical inference in massive datastreams
Jingshen Wang, Lilun Du, Changliang Zou +1
Modern technological advances have expanded the scope of applications requiring analysis of large-scale datastreams that comprise multiple indefinitely long time series. There is a…
A New Procedure for Controlling False Discovery Rate in Large-Scale t-tests
Changliang Zou, Haojie Ren, Xu Guo +1
This paper is concerned with false discovery rate (FDR) control in large-scale multiple testing problems. We first propose a new data-driven testing procedure for controlling the F…
False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation
Lilun Du, Xu Guo, Wenguang Sun +1
We develop a new class of distribution--free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized…
Nonparametric maximum likelihood approach to multiple change-point problems
Changliang Zou, Guosheng Yin, Long Feng +1
In multiple change-point problems, different data segments often follow different distributions, for which the changes may occur in the mean, scale or the entire distribution from…
Coordinate-independent sparse sufficient dimension reduction and variable selection
Xin Chen, Changliang Zou, R. Dennis Cook
Sufficient dimension reduction (SDR) in regression, which reduces the dimension by replacing original predictors with a minimal set of their linear combinations without loss of inf…