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20172025
most citedValid Post-Detection Inference for Change Points Identified Using Trend Filtering

4 citations · 23 across the 17 of their papers we have counts for

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11 papers · 1 filter

stat.ME2025

Graph-based Change Point Detection for Functional Data

Jeremy VanderDoes, Shojaeddin Chenouri

Modeling functions that are sequentially observed as functional time series is becoming increasingly common. In such models, it is often crucial to ensure data homogeneity. We inve…

stat.ME2021★ 2 cited

Robust changepoint detection in the variability of multivariate functional data

Kelly Ramsay, Shoja'eddin Chenouri

We consider the problem of robustly detecting changepoints in the variability of a sequence of independent multivariate functions. We develop a novel changepoint procedure, called…

stat.ME2021★ 1 cited

On Monitoring High-Dimensional Processes with Individual Observations

Mohsen Ebadi, Shojaeddin Chenouri, Stefan H. Steiner

Modern data collecting methods and computation tools have made it possible to monitor high-dimensional processes. In this article, Phase II monitoring of high-dimensional processes…

stat.ME2021

Phase I Analysis of High-Dimensional Processes in the Presence of Outliers

Mohsen Ebadi, Shojaeddin Chenouri, Stefan H. Steiner

One of the significant challenges in monitoring the quality of products today is the high dimensionality of quality characteristics. In this paper, we address Phase I analysis of h…

stat.ME2021★ 2 cited

Robust nonparametric hypothesis tests for differences in the covariance structure of functional data

Kelly Ramsay, Shojaeddin Chenouri

We develop a group of robust, nonparametric hypothesis tests which detect differences between the covariance operators of several populations of functional data. These tests, calle…

stat.ME2021★ 4 cited

Valid Post-Detection Inference for Change Points Identified Using Trend Filtering

Reza Valiollahi Mehrizi, Shojaeddin Chenouri

There are many research works and methods about change point detection in the literature. However, there are only a few that provide inference for such change points after being es…

stat.ME2020

Robust multiple change-point detection for multivariate variability using data depth

Kelly Ramsay, Shoja'eddin Chenouri

In this paper, we introduce two robust, nonparametric methods for multiple change-point detection in the variability of a multivariate sequence of observations. We demonstrate that…

stat.ME2020★ 2 cited

Detection of Change Points in Piecewise Polynomial Signals Using Trend Filtering

Reza V. Mehrizi, Shojaeddin Chenouri

While many approaches have been proposed for discovering abrupt changes in piecewise constant signals, few methods are available to capture these changes in piecewise polynomial si…

stat.ME2020

Statistical Monitoring of the Covariance Matrix in Multivariate Processes: A Literature Review

Mohsen Ebadi, Shoja'eddin Chenouri, Dennis K. J. Lin +1

Monitoring several correlated quality characteristics of a process is common in modern manufacturing and service industries. Although a lot of attention has been paid to monitoring…

stat.ME2017★ 3 cited

A New Method for Performance Analysis in Nonlinear Dimensionality Reduction

Jiaxi Liang, Shojaeddin Chenouri, Christopher G. Small

In this paper, we develop a local rank correlation measure which quantifies the performance of dimension reduction methods. The local rank correlation is easily interpretable, and…

stat.ME2017

Optimal estimation in functional linear regression for sparse noise-contaminated data

Behdad Mostafaiy, MohammadReza FaridRohani, Shojaeddin Chenouri

In this paper, we propose a novel approach to fit a functional linear regression in which both the response and the predictor are functions of a common variable such as time. We co…