Binghui Liu
Biographic Data
| ID | 8920498 |
|---|---|
| NAME | Binghui Liu |
| GIVEN NAMES | Binghui |
| FAMILY NAME | Liu |
| SIGNATURE | LIU B |
| AFFILIATIONS | Northeast Normal University |
| ORCID | 0000-0002-9331-8389 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
A One-Sided Refined Symmetrized Data Aggregation Approach to Robust Mutual Fund Selection
We consider the problem of identifying skilled funds among a large number of candidates under the linear factor pricing models containing both observable and latent market factors. Motivated by the existence of non-strong potential factors and diversity of error distribution types of the linear factor pricing models, we develop a distribution-free multiple testing procedure to solve this problem. The proposed procedure is established based on the…
An Inverse Norm Sign Test of Location Parameter for High-Dimensional Data
We consider the one sample location testing problem for high-dimensional data, where the data dimension is potentially much larger than the sample size. We devise a novel inverse norm sign test (INST) that is consistent and has much improved power than many existing popular tests. We further construct a general class of weighted spatial sign tests which includes these existing tests, and show that INST is the optimal member within this class, in …
Rank‐based Tests for Cross‐sectional Dependence in Large ( N , T ) Fixed Effects Panel Data Models
Most existing methods for testing cross‐sectional dependence in fixed effects panel data models are actually conducting tests for cross‐sectional uncorrelation, which are not robust to departures of normality of the error distributions as well as nonlinear cross‐sectional dependence. To this end, we construct two rank‐based tests for (static and dynamic) fixed effects panel data models, based on two very popular rank correlations, that is, Kendal…
No prominent works on this page.
Rank‐based Tests for Cross‐sectional Dependence in Large ( N , T ) Fixed Effects Panel Data Models
Most existing methods for testing cross‐sectional dependence in fixed effects panel data models are actually conducting tests for cross‐sectional uncorrelation, which are not robust to departures of normality of the error distributions as well as nonlinear cross‐sectional dependence. To this end, we construct two rank‐based tests for (static and dynamic) fixed effects panel data models, based on two very popular rank correlations, that is, Kendal…
An Inverse Norm Sign Test of Location Parameter for High-Dimensional Data
We consider the one sample location testing problem for high-dimensional data, where the data dimension is potentially much larger than the sample size. We devise a novel inverse norm sign test (INST) that is consistent and has much improved power than many existing popular tests. We further construct a general class of weighted spatial sign tests which includes these existing tests, and show that INST is the optimal member within this class, in …
A One-Sided Refined Symmetrized Data Aggregation Approach to Robust Mutual Fund Selection
We consider the problem of identifying skilled funds among a large number of candidates under the linear factor pricing models containing both observable and latent market factors. Motivated by the existence of non-strong potential factors and diversity of error distribution types of the linear factor pricing models, we develop a distribution-free multiple testing procedure to solve this problem. The proposed procedure is established based on the…
Mathematics (3 works) · Statistics (3 works) · Computer Science (2 works) · Econometrics (2 works) · Mathematical optimization (2 works) · Monte Carlo method (2 works) · Null hypothesis (2 works) · Advanced Statistical Methods and Models (1 works) · Applied Mathematics (1 works) · Asymptotic distribution (1 works)