22 citations · 54 across the 11 of their papers we have counts for
11 papers
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…
Inference on the Change Point for High Dimensional Dynamic Graphical Models
Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis +1
We develop an estimator for the change point parameter for a dynamically evolving graphical model, and also obtain its asymptotic distribution under high dimensional scaling. To pr…
Inference on the change point with the jump size near the boundary of the region of detectability in high dimensional time series models
Abhishek Kaul, Venkata K Jandhyala, Stergios B Fotopoulos
We develop a projected least squares estimator for the change point parameter in a high dimensional time series model with a potential change point. Importantly we work under the s…
Detection and estimation of parameters in high dimensional multiple change point regression models via regularization and discrete optimization
Abhishek Kaul, Venkata K Jandhyala, Stergios B Fotopoulos
Binary segmentation, which is sequential in nature is thus far the most widely used method for identifying multiple change points in statistical models. Here we propose a top down…