This paper considers the problem of detecting the common gradual changepoint in panel data. Given the situation that each panel/series has the common ......
Nonparametric regression is of primary importance in many statistical applications. For the data with censored outcomes, how to construct a confidence......
Many applications of regression study the predictors with complex forms such as tensors. Besides low dimensional assumption, the effects of predictors......
This paper studies the problem of classifying longitudinal structural brain networks to identify meaningful substructures and their time-varying effec......
Sliced uniform designs are useful for generating experimental data with batch structures. In this paper, we employ the discrete discrepancy as the mea......
In this article, a weighted empirical likelihood technique for constructing the empirical likelihood confidence regions is applied to study the hetero......
Quantile regression estimate gives more complete information about the response distribution but is more costly to compute than mean regression. When ......
In this paper, we consider the problem of obtaining appropriate weights for model averaging in the face of model uncertainty and outliers. Most of the......
The coronavirus disease 2019 (COVID-19) pandemic has led to tremendous loss of human life and has severe social and economic impacts worldwide. The sp......
Function-on-function linear regression is an essential tool in characterizing the linear relationship between a functional response and a functional p......
In this paper, we propose to use sufficient dimension reduction (SDR) in conjunction with nonparametric techniques to estimate the average treatment e......
Multi-layer networks are often used to represent multiple types of relationships between nodes in network studies. In this paper. we investigate the c......
In this article, we revisit the problem of how to construct better nonparametric confidence intervals for the conditional quantile function from an op......
Variable screening is of fundamental importance in linear regression models when the number of predictors far exceeds the number of observations. Mult......
As one of the most rapidly developing artificial intelligence techniques, deep learning has been applied in various machine learning tasks and has rec......
