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20182021
most citedOptimal nonparametric change point detection and localization

21 citations · 31 across the 3 of their papers we have counts for

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6 papers · 1 filter

stat.ME2021

Adversarially robust change point detection

Mengchu Li, Yi Yu

Change point detection is becoming increasingly popular in many application areas. On one hand, most of the theoretically-justified methods are investigated in an ideal setting wit…

stat.ME2020

Localizing Changes in High-Dimensional Regression Models

Alessandro Rinaldo, Daren Wang, Qin Wen +2

This paper addresses the problem of localizing change points in high-dimensional linear regression models with piecewise constant regression coefficients. We develop a dynamic prog…

stat.ME20201 cited

Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes

Daren Wang, Yi Yu, Rebecca Willett

High-dimensional self-exciting point processes have been widely used in many application areas to model discrete event data in which past and current events affect the likelihood o…

stat.ME201921 cited

Optimal nonparametric change point detection and localization

Oscar Hernan Madrid Padilla, Yi Yu, Daren Wang +1

We study change point detection and localization for univariate data in fully nonparametric settings in which, at each time point, we acquire an i.i.d. sample from an unknown distr…

stat.ME2018

Optimal Change Point Detection and Localization in Sparse Dynamic Networks

Daren Wang, Yi Yu, Alessandro Rinaldo

We study the problem of change point localization in dynamic networks models. We assume that we observe a sequence of independent adjacency matrices of the same size, each correspo…

stat.ME2018

Confidence intervals for high-dimensional Cox models

Yi Yu, Jelena Bradic, Richard J. Samworth

The purpose of this paper is to construct confidence intervals for the regression coefficients in high-dimensional Cox proportional hazards regression models where the number of co…