Heng Lian
Biographic Data
| ID | 8920532 |
|---|---|
| NAME | Heng Lian |
| GIVEN NAMES | Heng |
| FAMILY NAME | Lian |
| SIGNATURE | LIAN H |
| AFFILIATIONS | City University of Hong Kong |
| ORCID | 0000-0002-6008-6614 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Corporate Probability of Default
Corporate probability of default (PD) prediction is vitally important for risk management and asset pricing. In search of accurate PD prediction, we propose a flexible yet easy-to-interpret default-prediction single-index hazard model (DSI). By applying it to a comprehensive U.S. corporate bankruptcy database we constructed, we discover an interesting V-shaped relationship, indicating a violation of the common linear hazard specification. Most im…
Nonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models
Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since the efficiency of resulting estimators can be improved significantly by pooling the information of individuals within each group. However, the currently assumed parametric and semiparametric relationship between the response and predictors may be misspecified, which leads to a wrong grouping result, and the nonparametric ap…
High-Dimensional Dynamic Covariance Matrices With Homogeneous Structure
High-dimensional covariance matrices appear in many disciplines. Much literature has devoted to the research in high-dimensional constant covariance matrices. However, constant covariance matrices are not sufficient in applications, for example, in portfolio allocation, dynamic covariance matrices would be more appropriate. As argued in this article, there are two difficulties in the introduction of dynamic structures into covariance matrices: (1…
Homogeneity Pursuit in Single Index Models based Panel Data Analysis
Panel data analysis is an important topic in statistics and econometrics. Traditionally, in panel data analysis, all individuals are assumed to share the same unknown parameters, e.g. the same coefficients of covariates when the linear models are used, and the differences between the individuals are accounted for by cluster effects. This kind of modelling only makes sense if our main interest is on the global trend, this is because it would not b…
Semiparametric Estimation of Additive Quantile Regression Models by Two-Fold Penalty
In this article, we propose a model selection and semiparametric estimation method for additive models in the context of quantile regression problems. In particular, we are interested in finding nonzero components as well as linear components in the conditional quantile function. Our approach is based on spline approximation for the components aided by two Smoothly Clipped Absolute Deviation (SCAD) penalty terms. The advantage of our approach is …
No prominent works on this page.
Semiparametric Estimation of Additive Quantile Regression Models by Two-Fold Penalty
In this article, we propose a model selection and semiparametric estimation method for additive models in the context of quantile regression problems. In particular, we are interested in finding nonzero components as well as linear components in the conditional quantile function. Our approach is based on spline approximation for the components aided by two Smoothly Clipped Absolute Deviation (SCAD) penalty terms. The advantage of our approach is …
Homogeneity Pursuit in Single Index Models based Panel Data Analysis
Panel data analysis is an important topic in statistics and econometrics. Traditionally, in panel data analysis, all individuals are assumed to share the same unknown parameters, e.g. the same coefficients of covariates when the linear models are used, and the differences between the individuals are accounted for by cluster effects. This kind of modelling only makes sense if our main interest is on the global trend, this is because it would not b…
High-Dimensional Dynamic Covariance Matrices With Homogeneous Structure
High-dimensional covariance matrices appear in many disciplines. Much literature has devoted to the research in high-dimensional constant covariance matrices. However, constant covariance matrices are not sufficient in applications, for example, in portfolio allocation, dynamic covariance matrices would be more appropriate. As argued in this article, there are two difficulties in the introduction of dynamic structures into covariance matrices: (1…
Corporate Probability of Default
Corporate probability of default (PD) prediction is vitally important for risk management and asset pricing. In search of accurate PD prediction, we propose a flexible yet easy-to-interpret default-prediction single-index hazard model (DSI). By applying it to a comprehensive U.S. corporate bankruptcy database we constructed, we discover an interesting V-shaped relationship, indicating a violation of the common linear hazard specification. Most im…
Nonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models
Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since the efficiency of resulting estimators can be improved significantly by pooling the information of individuals within each group. However, the currently assumed parametric and semiparametric relationship between the response and predictors may be misspecified, which leads to a wrong grouping result, and the nonparametric ap…
Computer Science (5 works) · Mathematics (5 works) · Statistics (5 works) · Econometrics (4 works) · Estimator (3 works) · Statistical Methods and Inference (3 works) · Artificial Intelligence (2 works) · Covariate (2 works) · Mathematical optimization (2 works) · Nonparametric statistics (2 works)