A5102988442- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
- Additive Manufacturing Materials and Processes
- Advanced Measurement and Metrology Techniques
- Welding Techniques and Residual Stresses
- Advanced Statistical Methods and Models
- Manufacturing Process and Optimization
- Machine Learning and Data Classification
- Surface Roughness and Optical Measurements
- Optical measurement and interference techniques
- Data Analysis with R
- Genetic and phenotypic traits in livestock
- Spectroscopy and Laser Applications
- Fault Detection and Control Systems
- Additive Manufacturing and 3D Printing Technologies
- Gaussian Processes and Bayesian Inference
- Advanced Causal Inference Techniques
- Advanced Semiconductor Detectors and Materials
- Advanced Numerical Analysis Techniques
- Risk and Safety Analysis
- Laser Design and Applications
- Bayesian Methods and Mixture Models
- Solid State Laser Technologies
- Laser Material Processing Techniques
- Digital Filter Design and Implementation
Xiamen University
2022-2024
Tsinghua University
2019-2023
Institute of Chemistry
1996
Beijing National Laboratory for Molecular Sciences
1996
While deep learning approaches to survival data have demonstrated empirical success in applications, most of these methods are difficult interpret and mathematical understanding them is lacking. This paper studies the partially linear Cox model, where nonlinear component model implemented using a neural network. The proposed approach flexible able circumvent curse dimensionality, yet it facilitates interpretability effects treatment covariates on survival. We establish asymptotic theories...
Deep learning has enjoyed tremendous success in a variety of applications but its application to quantile regressions remains scarce. A major advantage the deep approach is flexibility model complex data more parsimonious way than nonparametric smoothing methods. However, while brought breakthroughs prediction, it often lacks interpretability due black-box nature multilayer structure with millions parameters, hence not well suited for statistical inference. In this paper, we leverage...
Summary Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption that there are enough domain interest to estimate both functions. We investigate estimation snippets which observations from subject available only an interval length strictly, often much, shorter than whole interest. For such sampling plan, no direct off-diagonal region function. tackle challenge via basis...
In medical studies, time-to-event outcomes such as time to death or relapse of a disease are routinely recorded along with longitudinal data that observed intermittently during the follow-up period. For various reasons, marginal approaches model event time, corresponding separate for survival data/longitudinal data, tend induce bias and lose efficiency. Instead, joint modeling approach brings two types together can reduce eliminate yield more efficient estimation procedure. A...
Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption that there are enough domain interest to estimate both functions. In paper, we investigate estimation snippets which observations from subject available only an interval length strictly (and often much) shorter than whole interest. For such sampling plan, no direct off-diagonal region function. We tackle challenge via basis...
Directed Energy Deposition (DED) technology plays an important role in metal additive manufacturing and remanufacturing. However, the quality repeatability of DED products are still regarded as a challenge that hinders their widespread applications, especially cutting-edge sectors. As promising approach to meet challenge, in-situ monitoring mechanical quantities during process, such deformation stress, has attracted extensive attention both academic engineering fields. In this study,...