3 citations · 7 across the 5 of their papers we have counts for
5 papers · 1 filter
Inference for Change Points in High Dimensional Mean Shift Models
Abhishek Kaul, George Michailidis
We consider the problem of constructing confidence intervals for the locations of change points in a high-dimensional mean shift model. To that end, we develop a locally refitted l…
Segmentation of high dimensional means over multi-dimensional change points and connections to regression trees
Abhishek Kaul
This article is motivated by the objective of providing a new analytically tractable and fully frequentist framework to characterize and implement regression trees while also allow…
Inference on the change point in high dimensional time series models via plug in least squares
Abhishek Kaul, Stergios B. Fotopoulos, Venkata K. Jandhyala +1
We study a plug in least squares estimator for the change point parameter where change is in the mean of a high dimensional random vector under subgaussian or subexponential distri…
An efficient two step algorithm for high dimensional change point regression models without grid search
Abhishek Kaul, Venkata K. Jandhyala, Stergios B. Fotopoulos
We propose a two step algorithm based on regularization for the detection and estimation of parameters of a high dimensional change point regression model and provi…
Two Stage Non-penalized Corrected Least Squares for High Dimensional Linear Models with Measurement error or Missing Covariates
Abhishek Kaul, Hira L. Koul, Akshita Chawla +1
This paper provides an alternative to penalized estimators for estimation and vari- able selection in high dimensional linear regression models with measurement error or missing co…