What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Random variable
Wei Biao Wu focuses on Applied mathematics, Covariance, Econometrics, Covariance matrix and Stationary process.
Wei Biao Wu interconnects Estimator, Asymptotic distribution, Central limit theorem and Series in the investigation of issues within Applied mathematics.
His study in Central limit theorem is interdisciplinary in nature, drawing from both Martingale, Probability theory and Mathematical analysis.
His Covariance study is related to the wider topic of Statistics.
The various areas that Wei Biao Wu examines in his Econometrics study include Stochastic process, Multiple comparisons problem, Mathematical statistics and Statistical hypothesis testing.
His work deals with themes such as Confidence and prediction bands and Autocorrelation, which intersect with Stationary process.
His most cited work include:
- Nonlinear system theory: Another look at dependence (371 citations)
- Nonparametric estimation of large covariance matrices of longitudinal data (252 citations)
- STRONG INVARIANCE PRINCIPLES FOR DEPENDENT RANDOM VARIABLES (189 citations)
What are the main themes of his work throughout his whole career to date?
Wei Biao Wu spends much of his time researching Applied mathematics, Series, Econometrics, Statistics and Central limit theorem.
The various areas that he examines in his Applied mathematics study include Nonparametric statistics, Mathematical analysis, Covariance, Estimator and Mathematical optimization.
His Covariance research includes themes of Rate of convergence and Covariance matrix.
His Series study integrates concerns from other disciplines, such as Convergence, Inference, Range, Consistency and Moment.
In the field of Econometrics, his study on Quantile overlaps with subjects such as Long-term prediction.
Wei Biao Wu has included themes like Asymptotic theory, Limit, Markov chain, Range and Random function in his Central limit theorem study.
He most often published in these fields:
- Applied mathematics (50.29%)
- Series (25.14%)
- Econometrics (20.57%)
What were the highlights of his more recent work (between 2018-2021)?
- Series (25.14%)
- Applied mathematics (50.29%)
- Econometrics (20.57%)
In recent papers he was focusing on the following fields of study:
His main research concerns Series, Applied mathematics, Econometrics, Moment and Inference.
The Series study combines topics in areas such as High dimensional, Kernel density estimation and Asymptotic distribution.
His studies deal with areas such as Nonparametric statistics, Covariance matrix and Parametric statistics as well as Asymptotic distribution.
His work carried out in the field of Applied mathematics brings together such families of science as Autoregressive conditional heteroskedasticity, Estimator, Central limit theorem and Consistency.
His Econometrics research is multidisciplinary, incorporating elements of Prediction interval and Bayesian probability.
His study in Inference is interdisciplinary in nature, drawing from both Logarithm and Regression.
Between 2018 and 2021, his most popular works were:
- Towards a general theory for nonlinear locally stationary processes (20 citations)
- Testing for Trends in High-Dimensional Time Series (11 citations)
- Simultaneous inference for time-varying models (5 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Random variable
His scientific interests lie mostly in Applied mathematics, Series, Econometrics, Estimator and Approximation theory.
Wei Biao Wu integrates many fields in his works, including Applied mathematics and General theory.
His studies in Series integrate themes in fields like Sample size determination, Stochastic process, Measure, Independence and Range.
His Econometrics research incorporates themes from Bayesian probability and Bayes' theorem.
His research in Estimator intersects with topics in Nonparametric statistics, Statistical inference, Parametric statistics and High dimensional.
The concepts of his Approximation theory study are interwoven with issues in Statistics and Chi-square test.
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