Huixia Judy Wang
Biographic Data
| ID | 8920165 |
|---|---|
| NAME | Huixia Judy Wang |
| GIVEN NAMES | Huixia Judy |
| FAMILY NAME | Wang |
| SIGNATURE | WANG H J |
| AFFILIATIONS | Department of Statistics, The George Washington University, Washington, DC ( ) |
| ORCID | 0000-0002-5195-8564 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Gini Variance Estimation of Grouped Data
We propose a jackknife variance estimator for the Gini Index based on grouped data. It only requires access to group means and counts, and is computationally fast, modifying an existing algorithm that exploits the Gini's connection with regression modelling. After reviewing the group‐level point estimator, we discuss its asymptotic normality and our jackknife's consistency. We then conduct a multiplicative random effects simulation, comparing the…
Extremal local linear quantile regression for heavy-tailed time series with near epoch dependence
This paper develops a data-driven inference procedure for extreme analysis of data with near epoch dependence (NED), a condition less restrictive than traditional dependence structures like α-mixing, making it particularly useful for analyzing heavy-tailed time series data. To capture nonlinear data structures, we propose a new framework that combines local linear quantile regression with extreme value theory. We first study the asymptotic proper…
Extreme Quantile Estimation for Autoregressive Models
A quantile autoregresive model is a useful extension of classical autoregresive models as it can capture the influences of conditioning variables on the location, scale, and shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this article, assuming quantile autoregresive models, we develop a new estimator for extreme conditional quantiles of time …
No prominent works on this page.
Extreme Quantile Estimation for Autoregressive Models
A quantile autoregresive model is a useful extension of classical autoregresive models as it can capture the influences of conditioning variables on the location, scale, and shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this article, assuming quantile autoregresive models, we develop a new estimator for extreme conditional quantiles of time …
Gini Variance Estimation of Grouped Data
We propose a jackknife variance estimator for the Gini Index based on grouped data. It only requires access to group means and counts, and is computationally fast, modifying an existing algorithm that exploits the Gini's connection with regression modelling. After reviewing the group‐level point estimator, we discuss its asymptotic normality and our jackknife's consistency. We then conduct a multiplicative random effects simulation, comparing the…
Extremal local linear quantile regression for heavy-tailed time series with near epoch dependence
This paper develops a data-driven inference procedure for extreme analysis of data with near epoch dependence (NED), a condition less restrictive than traditional dependence structures like α-mixing, making it particularly useful for analyzing heavy-tailed time series data. To capture nonlinear data structures, we propose a new framework that combines local linear quantile regression with extreme value theory. We first study the asymptotic proper…
Statistical Methods and Inference (3 works) · Advanced Statistical Methods and Models (2 works) · Estimation (2 works) · Financial Risk and Volatility Modeling (2 works) · Quantile (2 works) · Archaeology (1 works) · Artificial Intelligence (1 works) · Autoregressive model (1 works) · Bayesian Methods and Mixture Models (1 works) · China (1 works)