Markus Pelger
Biographic Data
| ID | 8920273 |
|---|---|
| NAME | Markus Pelger |
| GIVEN NAMES | Markus |
| FAMILY NAME | Pelger |
| SIGNATURE | PELGER M |
| AFFILIATIONS | Stanford University |
| ORCID | 0000-0001-7111-3588 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
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
Discussion of “Text Selection” by Bryan Kelly, Asaf Manela, and Alan Moreira
The article addresses the important question of how to infer information from text data, which is a high-dimensional form of data. The article proposes to estimate separately the probability of sel
No prominent works on this page.
Discussion of “Text Selection” by Bryan Kelly, Asaf Manela, and Alan Moreira
The article addresses the important question of how to infer information from text data, which is a high-dimensional form of data. The article proposes to estimate separately the probability of sel
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
Artificial Intelligence (2 works) · Complex Systems and Time Series Analysis (2 works) · Computer Science (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 Text Analysis Techniques (1 work)