Yanyuan Ma
Biographic Data
| ID | 8920499 |
|---|---|
| NAME | Yanyuan Ma |
| GIVEN NAMES | Yanyuan |
| FAMILY NAME | Ma |
| SIGNATURE | MA Y |
| AFFILIATIONS | Department of Statistics, Pennsylvania State University, University Park, PA |
| ORCID | 0000-0001-6985-0351 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Prediction Using Many Samples with Models Possibly Containing Partially Shared Parameters
We consider prediction based on a main model. When the main model shares partial parameters with several other helper models, we make use of the additional information. Specifically, we propose a Model Averaging Prediction (MAP) procedure that takes into account data related to the main model as well as data related to the helper models. We allow the data related to different models to follow different structures, as long as they share some commo…
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 …
No prominent works on this page.
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 …
Prediction Using Many Samples with Models Possibly Containing Partially Shared Parameters
We consider prediction based on a main model. When the main model shares partial parameters with several other helper models, we make use of the additional information. Specifically, we propose a Model Averaging Prediction (MAP) procedure that takes into account data related to the main model as well as data related to the helper models. We allow the data related to different models to follow different structures, as long as they share some commo…
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…
Computer Science (3 works) · Mathematics (3 works) · Econometrics (2 works) · Machine learning (2 works) · Mathematical optimization (2 works) · Statistical Methods and Inference (2 works) · Statistics (2 works) · Advanced Statistical Methods and Models (1 works) · Applied Mathematics (1 works) · Artificial Intelligence (1 works)