What is he best known for?
The fields of study he is best known for:
- Statistics
- Regression analysis
- Mathematical analysis
His primary scientific interests are in Applied mathematics, Estimator, Statistics, Econometrics and Series.
His Applied mathematics research is multidisciplinary, incorporating perspectives in Parametric statistics, Linear regression, Linear model, Conditional variance and Regression analysis.
In his study, Cluster-weighted modeling is inextricably linked to Conditional probability distribution, which falls within the broad field of Estimator.
The concepts of his Econometrics study are interwoven with issues in Time series and Local regression.
His research in Series intersects with topics in Curse of dimensionality, Portmanteau test, Dimensionality reduction, Factor analysis and Calculus.
His studies deal with areas such as Autoregressive conditional heteroskedasticity and Autoregressive model as well as Asymptotic distribution.
His most cited work include:
- Nonlinear Time Series: Nonparametric and Parametric Methods (1040 citations)
- Functional-Coefficient Regression Models for Nonlinear Time Series (415 citations)
- Efficient Estimation of Conditional Variance Functions in Stochastic Regression (368 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Applied mathematics, Estimator, Statistics, Series and Econometrics.
His research integrates issues of Nonparametric statistics, Autocovariance, Linear model, Mathematical optimization and Conditional probability distribution in his study of Applied mathematics.
His Estimator study combines topics from a wide range of disciplines, such as Parametric statistics, Autoregressive model and Time series.
In the subject of general Statistics, his work in Nonparametric regression, Statistical hypothesis testing and Random variable is often linked to Function, thereby combining diverse domains of study.
His study looks at the relationship between Series and fields such as Dimensionality reduction, as well as how they intersect with chemical problems.
His Econometrics study frequently involves adjacent topics like Multivariate statistics.
He most often published in these fields:
- Applied mathematics (50.58%)
- Estimator (50.19%)
- Statistics (29.57%)
What were the highlights of his more recent work (between 2014-2021)?
- Applied mathematics (50.58%)
- Estimator (50.19%)
- Series (25.29%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Applied mathematics, Estimator, Series, Econometrics and Algorithm.
He has included themes like Nonparametric statistics, Autocovariance, Sample size determination, Inference and Autoregressive model in his Applied mathematics study.
His specific area of interest is Estimator, where Qiwei Yao studies Asymptotic distribution.
His Series research includes elements of Omnibus test, Principal component analysis, Cointegration and White noise.
The Econometrics study combines topics in areas such as Generalized linear model and Linear regression.
In his study, Kernel density estimation, Portmanteau test, Multivariate normal distribution, Multivariate random variable and Time series is strongly linked to Independent and identically distributed random variables, which falls under the umbrella field of Algorithm.
Between 2014 and 2021, his most popular works were:
- Modelling and forecasting daily electricity load curves: a hybrid approach (36 citations)
- High dimensional stochastic regression with latent factors, endogeneity and nonlinearity (33 citations)
- High dimensional stochastic regression with latent factors, endogeneity and nonlinearity (33 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Regression analysis
- Mathematical analysis
His primary scientific interests are in Applied mathematics, Estimator, Econometrics, Endogeneity and Series.
The study incorporates disciplines such as Asymptotic theory, Autocovariance, Sample size determination, Inference and Autoregressive model in addition to Applied mathematics.
His studies link Parametric statistics with Estimator.
His work on Probit model as part of his general Econometrics study is frequently connected to Estimation, thereby bridging the divide between different branches of science.
His Endogeneity research incorporates themes from Linear regression, Time series, Regression, Nonlinear regression and Factor analysis.
His Series research focuses on Cointegration and how it relates to Vector autoregression.
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