79 citations · 84 across the 4 of their papers we have counts for
4 papers
Higher Order Refinements by Bootstrap in Lasso and other Penalized Regression Methods
Debraj Das, Arindam Chatterjee, S. N. Lahiri
Selection of important covariates and to drop the unimportant ones from a high-dimensional regression model is a long standing problem and hence have received lots of attention in…
Inference problems in binary regression model with misclassified responses
Arindam Chatterjee, Tathagata Bandyopadhyay, Sumanta Adhya
Misclassification of binary responses, if ignored, may severely bias the maximum likelihood estimators (MLE) of regression parameters. For such data, a binary regression model inco…
Rates of convergence of the Adaptive LASSO estimators to the Oracle distribution and higher order refinements by the bootstrap
A. Chatterjee, S. N. Lahiri
Zou [J. Amer. Statist. Assoc. 101 (2006) 1418-1429] proposed the Adaptive LASSO (ALASSO) method for simultaneous variable selection and estimation of the regression parameters, and…
A Sub-Gaussian Berry-Esseen Theorem for the Hypergeometric Distribution
Soumendra N. Lahiri, A. Chatterjee, T. Maiti
In this paper, we derive a necessary and sufficient condition on the parameters of the Hypergeometric distribution for weak convergence to a Normal limit. We establish a Berry-Esse…