79 citations · 82 across the 6 of their papers we have counts for
6 papers
Large Sample Properties of Higher Order Markov Models
Tuhin Majumder, Donald E. K. Martin, Soumendra N. Lahiri
We study large-sample properties of higher-order Markov chains on a finite alphabet when the order is allowed to grow with the sequence length . By embedding the proce…
On Optimal Data Splitting for Split Conformal Prediction
Sayan Das, Bahram Yaghooti, Todd A. Kuffner +1
Conformal prediction and its variants, including the split conformal prediction, provide a distribution-free framework for uncertainty quantification by constructing prediction int…
Multi-Resolution Analysis of Variable Selection for Road Safety in St. Louis and Its Neighboring Area
Debjoy Thakur, Soumendra N. Lahiri
Generally, Lasso, Adaptive Lasso, and SCAD are standard approaches in variable selection in the presence of a large number of predictors. In recent years, during intensity function…
Fitting Sparse Markov Models to Categorical Time Series Using Convex Clustering
Tuhin Majumder, Soumendra Lahiri, Donald Martin
Higher-order Markov chains are frequently used to model categorical time series. However, a major problem with fitting such models is the exponentially growing number of parameters…
Perturbation Bootstrap in Adaptive Lasso
Debraj Das, Karl Gregory, S. N. Lahiri
The Adaptive Lasso(Alasso) was proposed by Zou [\textit{J. Amer. Statist. Assoc. \textbf{101} (2006) 1418-1429}] as a modification of the Lasso for the purpose of simultaneous vari…
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…