What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Random variable
His primary areas of study are Statistics, Asymptotic distribution, Estimator, Extreme value theory and Econometrics.
Empirical likelihood, Tail index, Heavy-tailed distribution, Heteroscedasticity and Consistent estimator are subfields of Statistics in which his conducts study.
His studies deal with areas such as Generalized integer gamma distribution, Pareto interpolation and Lomax distribution as well as Asymptotic distribution.
His study focuses on the intersection of Estimator and fields such as Conditional probability distribution with connections in the field of Autoregressive model and Least absolute deviations.
His Extreme value theory research is multidisciplinary, relying on both Mean squared error, Probability distribution and Order statistic, Applied mathematics.
His work on Quantile as part of general Econometrics research is frequently linked to Trace, bridging the gap between disciplines.
His most cited work include:
- Using a Bootstrap Method to Choose the Sample Fraction in Tail Index Estimation (344 citations)
- Comparison of tail index estimators (200 citations)
- Least absolute deviations estimation for ARCH and GARCH models (144 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Statistics, Estimator, Econometrics, Empirical likelihood and Applied mathematics.
His Confidence interval, Asymptotic distribution, Extreme value theory, Heavy-tailed distribution and Interval estimation study are his primary interests in Statistics.
As a member of one scientific family, Liang Peng mostly works in the field of Estimator, focusing on Mean squared error and, on occasion, Index.
Liang Peng has researched Econometrics in several fields, including Value at risk and Predictability.
The concepts of his Empirical likelihood study are interwoven with issues in Estimating equations, Jackknife resampling, Autoregressive model, Sample and Likelihood function.
His Applied mathematics study also includes
- Estimation theory most often made with reference to Parametric statistics,
- Tail dependence that connect with fields like Elliptical distribution,
- Bivariate analysis that connect with fields like Joint probability distribution.
He most often published in these fields:
- Statistics (57.21%)
- Estimator (34.62%)
- Econometrics (33.65%)
What were the highlights of his more recent work (between 2015-2021)?
- Econometrics (33.65%)
- Empirical likelihood (34.13%)
- Statistics (57.21%)
In recent papers he was focusing on the following fields of study:
Liang Peng mainly focuses on Econometrics, Empirical likelihood, Statistics, Asymptotic distribution and Nonparametric statistics.
His Econometrics study incorporates themes from Value at risk, Inference, Risk measure and House price index.
His studies in Empirical likelihood integrate themes in fields like Test, Predictability, Applied mathematics, Autoregressive model and Sample.
Statistics is closely attributed to Dynamic risk measure in his work.
His Asymptotic distribution study deals with Expected shortfall intersecting with Quantile.
Liang Peng has included themes like Heavy-tailed distribution and Limit in his Estimator study.
Between 2015 and 2021, his most popular works were:
- Inference for intermediate Haezendonck–Goovaerts risk measure (9 citations)
- Tail Dependence Measure for Examining Financial Extreme Co-movements (8 citations)
- An efficient approach to quantile capital allocation and sensitivity analysis (8 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Random variable
Liang Peng mainly investigates Econometrics, Nonparametric statistics, Copula, Inference and Risk measure.
His Econometrics research is multidisciplinary, incorporating elements of Test, Asymptotic distribution and Predictability.
His biological study deals with issues like Applied mathematics, which deal with fields such as Stochastic process and Multivariate t-distribution.
His Inference research focuses on Statistical inference and how it connects with Benchmark.
His Risk measure research integrates issues from Estimator and Delta method.
His Estimator study necessitates a more in-depth grasp of Statistics.
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