Laurent Callot
Biographic Data
| ID | 8920890 |
|---|---|
| NAME | Laurent Callot |
| GIVEN NAMES | Laurent |
| FAMILY NAME | Callot |
| SIGNATURE | CALLOT L |
| AFFILIATIONS | Amazon Research, Berlin, Germany |
| ORCID | 0000-0002-0756-8120 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
A Nodewise Regression Approach to Estimating Large Portfolios
This article investigates the large sample properties of the variance, weights, and risk of high-dimensional portfolios where the inverse of the covariance matrix of excess asset returns is estimated using a technique called nodewise regression. Nodewise regression provides a direct estimator for the inverse covariance matrix using the least absolute shrinkage and selection operator to estimate the entries of a sparse precision matrix. We show th…
Sharp Threshold Detection Based on Sup-Norm Error Rates in High-Dimensional Models
We propose a new estimator, the thresholded scaled Lasso, in high-dimensional threshold regressions. First, we establish an upper bound on the l∞ estimation error of the scaled Lasso estimator of Lee, Seo, and Shin. This is a nontrivial task as the literature on high-dimensional models has focused almost exclusively on l1 and l2 estimation errors. We show that this sup-norm bound can be used to distinguish between zero and nonzero coefficients at…
No prominent works on this page.
Sharp Threshold Detection Based on Sup-Norm Error Rates in High-Dimensional Models
We propose a new estimator, the thresholded scaled Lasso, in high-dimensional threshold regressions. First, we establish an upper bound on the l∞ estimation error of the scaled Lasso estimator of Lee, Seo, and Shin. This is a nontrivial task as the literature on high-dimensional models has focused almost exclusively on l1 and l2 estimation errors. We show that this sup-norm bound can be used to distinguish between zero and nonzero coefficients at…
A Nodewise Regression Approach to Estimating Large Portfolios
This article investigates the large sample properties of the variance, weights, and risk of high-dimensional portfolios where the inverse of the covariance matrix of excess asset returns is estimated using a technique called nodewise regression. Nodewise regression provides a direct estimator for the inverse covariance matrix using the least absolute shrinkage and selection operator to estimate the entries of a sparse precision matrix. We show th…
Econometrics (2 works) · Mathematics (2 works) · Statistics (2 works) · Advanced Statistical Methods and Models (1 work) · Bias of an estimator (1 work) · Complex Systems and Time Series Analysis (1 work) · Computer Science (1 work) · Covariance (1 work) · Covariance matrix (1 work) · Economics (1 work)