Ruoxuan Xiong
Biographic Data
| ID | 8920144 |
|---|---|
| NAME | Ruoxuan Xiong |
| GIVEN NAMES | Ruoxuan |
| FAMILY NAME | Xiong |
| SIGNATURE | XIONG R |
| AFFILIATIONS | Stanford University |
| ORCID | 0000-0002-3701-4428 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference
In many observational studies in social science and medicine, subjects or units are connected, and one unit’s treatment and attributes may affect another’s treatment and outcome, violating the stable unit treatment value assumption (SUTVA) and resulting in interference. To enable feasible estimation and inference, many previous works assume exchangeability of interfering units (neighbors). However, in many applications with distinctive units, int…
Interpretable Sparse Proximate Factors for Large Dimensions
This article proposes sparse and easy-to-interpret proximate factors to approximate statistical latent factors. Latent factors in a large-dimensional factor model can be estimated by principal component analysis (PCA), but are usually hard to interpret. We obtain proximate factors that are easier to interpret by shrinking the PCA factor weights and setting them to zero except for the largest absolute ones. We show that proximate factors construct…
State-Varying Factor Models of Large Dimensions
No prominent works on this page.
Interpretable Sparse Proximate Factors for Large Dimensions
This article proposes sparse and easy-to-interpret proximate factors to approximate statistical latent factors. Latent factors in a large-dimensional factor model can be estimated by principal component analysis (PCA), but are usually hard to interpret. We obtain proximate factors that are easier to interpret by shrinking the PCA factor weights and setting them to zero except for the largest absolute ones. We show that proximate factors construct…
State-Varying Factor Models of Large Dimensions
Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference
In many observational studies in social science and medicine, subjects or units are connected, and one unit’s treatment and attributes may affect another’s treatment and outcome, violating the stable unit treatment value assumption (SUTVA) and resulting in interference. To enable feasible estimation and inference, many previous works assume exchangeability of interfering units (neighbors). However, in many applications with distinctive units, int…
Complex Systems and Time Series Analysis (2 works) · Econometrics (2 works) · Factor analysis (2 works) · Financial Risk and Volatility Modeling (2 works) · Mathematics (2 works) · Principal component analysis (2 works) · Statistics (2 works) · Advanced Causal Inference Techniques (1 work) · Applied Mathematics (1 work) · Artificial Intelligence (1 work)