Weichi Wu
Biographic Data
| ID | 8920806 |
|---|---|
| NAME | Weichi Wu |
| GIVEN NAMES | Weichi |
| FAMILY NAME | Wu |
| SIGNATURE | WU W |
| AFFILIATIONS | Department of Statistics, Toronto, Ontario, M5S 3G3 Canada () |
| ORCID | 0000-0001-5716-0193 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Prediction in Locally Stationary Time Series
We develop an estimator for the high-dimensional covariance matrix of a locally stationary process with a smoothly varying trend and use this statistic to derive consistent predictors in nonstationary time series. In contrast to the currently available methods for this problem the predictor developed here does not rely on fitting an autoregressive model and does not require a vanishing trend. The finite sample properties of the new methodology ar…
Nonparametric Inference for Time-Varying Coefficient Quantile Regression
The article considers nonparametric inference for quantile regression models with time-varying coefficients. The errors and covariates of the regression are assumed to belong to a general class of locally stationary processes and are allowed to be cross-dependent. Simultaneous confidence tubes (SCTs) and integrated squared difference tests (ISDTs) are proposed for simultaneous nonparametric inference of the latter models with asymptotically corre…
No prominent works on this page.
Nonparametric Inference for Time-Varying Coefficient Quantile Regression
The article considers nonparametric inference for quantile regression models with time-varying coefficients. The errors and covariates of the regression are assumed to belong to a general class of locally stationary processes and are allowed to be cross-dependent. Simultaneous confidence tubes (SCTs) and integrated squared difference tests (ISDTs) are proposed for simultaneous nonparametric inference of the latter models with asymptotically corre…
Prediction in Locally Stationary Time Series
We develop an estimator for the high-dimensional covariance matrix of a locally stationary process with a smoothly varying trend and use this statistic to derive consistent predictors in nonstationary time series. In contrast to the currently available methods for this problem the predictor developed here does not rely on fitting an autoregressive model and does not require a vanishing trend. The finite sample properties of the new methodology ar…
Advanced Statistical Methods and Models (2 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Econometrics (2 works) · Financial Risk and Volatility Modeling (2 works) · Mathematics (2 works) · Statistics (2 works) · Applied Mathematics (1 works) · Autoregressive model (1 works) · Complex Systems and Time Series Analysis (1 works)