5 papers
Generalized Linear Models with Structured Sparsity Estimators
Mehmet Caner
In this paper, we introduce structured sparsity estimators in Generalized Linear Models. Structured sparsity estimators in the least squares loss are introduced by Stucky and van d…
Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso
Mehmet Caner, Kfir Eliaz
We consider situations where a user feeds her attributes to a machine learning method that tries to predict her best option based on a random sample of other users. The predictor i…
An Upper Bound for Functions of Estimators in High Dimensions
Mehmet Caner, Xu Han
We provide an upper bound as a random variable for the functions of estimators in high dimensions. This upper bound may help establish the rate of convergence of functions in high…
High Dimensional Linear GMM
Mehmet Caner, Anders Bredahl Kock
This paper proposes a desparsified GMM estimator for estimating high-dimensional regression models allowing for, but not requiring, many more endogenous regressors than observation…
Sharp Threshold Detection Based on Sup-norm Error rates in High-dimensional Models
Laurent Callot, Mehmet Caner, Anders Bredahl Kock +1
We propose a new estimator, the thresholded scaled Lasso, in high dimensional threshold regressions. First, we establish an upper bound on the estimation error of the…