Y K Tse
Biographic Data
| ID | 8880010 |
|---|---|
| NAME | Y K Tse |
| GIVEN NAMES | Y K |
| FAMILY NAME | Tse |
| SIGNATURE | TSE Y K |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2002 |
| LATEST PUBLICATION YEAR | 2007 |
| H-INDEX | 0 |
A Corrected Plug-in Method for Quantile Interval Construction Through a Transformed Regression
We propose a corrected plug-in method for constructing confidence intervals of the conditional quantiles of an original response variable through a transformed regression with heteroscedastic errors. The interval is easy to compute. Factors affecting the magnitude of the correction are examined analytically through the special case of Box–Cox regression. Monte Carlo simulations show that the new method works well in general and is superior over t…
A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations
In this article we propose a new multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model with time-varying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. Whereas each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analo…
No prominent works on this page.
A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations
In this article we propose a new multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model with time-varying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. Whereas each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analo…
A Corrected Plug-in Method for Quantile Interval Construction Through a Transformed Regression
We propose a corrected plug-in method for constructing confidence intervals of the conditional quantiles of an original response variable through a transformed regression with heteroscedastic errors. The interval is easy to compute. Factors affecting the magnitude of the correction are examined analytically through the special case of Box–Cox regression. Monte Carlo simulations show that the new method works well in general and is superior over t…
Econometrics (2 works) · Heteroscedasticity (2 works) · Mathematics (2 works) · Statistics (2 works) · Advanced Statistical Methods and Models (1 work) · Advanced Statistical Process Monitoring (1 work) · Autoregressive conditional heteroskedasticity (1 work) · Autoregressive model (1 work) · Conditional probability distribution (1 work) · Conditional variance (1 work)