7 citations · 8 across the 7 of their papers we have counts for
11 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…
Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models
Mehmet Caner, Marcelo Medeiros, Gabriel Vasconcelos
We provide a new theory for nodewise regression when the residuals from a fitted factor model are used. We apply our results to the analysis of the consistency of Sharpe ratio esti…
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…
Delta Theorem in the Age of High Dimensions
Mehmet Caner
We provide a new version of delta theorem, that takes into account of high dimensional parameter estimation. We show that depending on the structure of the function, the limits of…
A Nodewise Regression Approach to Estimating Large Portfolios
Laurent Callot, Mehmet Caner, Esra Ulasan +1
This paper 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 ret…
Inference in partially identified models with many moment inequalities using Lasso
Federico A. Bugni, Mehmet Caner, Anders Bredahl Kock +1
This paper considers inference in a partially identified moment (in)equality model with many moment inequalities. We propose a novel two-step inference procedure that combines the…
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…
Asymptotically Honest Confidence Regions for High Dimensional Parameters by the Desparsified Conservative Lasso
Mehmet Caner, Anders Bredahl Kock
In this paper we consider the conservative Lasso which we argue penalizes more correctly than the Lasso and show how it may be desparsified in the sense of van de Geer et al. (2014…
Oracle Inequalities for Convex Loss Functions with Non-Linear Targets
Mehmet Caner, Anders Bredahl Kock
This paper consider penalized empirical loss minimization of convex loss functions with unknown non-linear target functions. Using the elastic net penalty we establish a finite sam…