What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Machine learning
Zhiliang Ying mostly deals with Statistics, Estimator, Regression analysis, Econometrics and Covariate.
His research brings together the fields of Applied mathematics and Statistics.
His research investigates the connection between Estimator and topics such as Covariance matrix that intersect with problems in Resampling.
His work carried out in the field of Regression analysis brings together such families of science as Semiparametric model and Linear regression.
His studies in Covariate integrate themes in fields like Regression diagnostic and Regression.
His studies deal with areas such as Inference and Hazard as well as Proportional hazards model.
His most cited work include:
- Checking the Cox model with cumulative sums of martingale-based residuals (1072 citations)
- Semiparametric regression for the mean and rate functions of recurrent events (617 citations)
- Semiparametric analysis of the additive risk model (459 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Statistics, Estimator, Econometrics, Applied mathematics and Regression analysis.
His study in Covariate, Proportional hazards model, Linear regression, Accelerated failure time model and Counting process are all subfields of Statistics.
His work deals with themes such as Sample size determination and Regression, which intersect with Covariate.
His work in Proportional hazards model tackles topics such as Inference which are related to areas like Data mining.
The study incorporates disciplines such as Estimation theory and Mathematical optimization in addition to Estimator.
His Regression analysis research includes themes of Martingale, Resampling and Least absolute deviations.
He most often published in these fields:
- Statistics (49.48%)
- Estimator (36.08%)
- Econometrics (18.56%)
What were the highlights of his more recent work (between 2017-2021)?
- Data mining (10.31%)
- Machine learning (9.28%)
- Artificial intelligence (9.28%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Data mining, Machine learning, Artificial intelligence, Latent variable and Estimation theory.
Zhiliang Ying combines subjects such as Feature extraction and Inference with his study of Data mining.
His studies in Machine learning integrate themes in fields like Test, Sequence and Behavioral pattern.
His Behavioral pattern research is multidisciplinary, incorporating elements of Statistics and Cluster analysis.
His biological study spans a wide range of topics, including Differential item functioning, Item response theory, Local independence, Test theory and Graphical model.
His Estimator research includes themes of Class, Distribution and Resampling.
Between 2017 and 2021, his most popular works were:
- Recommendation System for Adaptive Learning. (29 citations)
- A reinforcement learning approach to personalized learning recommendation systems (15 citations)
- Statistical Analysis of Complex Problem-Solving Process Data: An Event History Analysis Approach. (11 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Machine learning
Zhiliang Ying spends much of his time researching Artificial intelligence, Machine learning, Data mining, Latent variable and Multidimensional scaling.
His Artificial intelligence study incorporates themes from Latent class model and Behavioral pattern.
His Machine learning research incorporates themes from Exploratory data analysis, Distribution and Documentation.
His study in Data mining is interdisciplinary in nature, drawing from both Psychometrics and Local independence.
His Psychometrics research is multidisciplinary, incorporating perspectives in Estimation theory, Item response theory, Differential item functioning, Test theory and Graphical model.
His work deals with themes such as Feature extraction, Process, Interpretability and Measure, which intersect with Multidimensional scaling.
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