Online Change-Point Detection of Linear Regression Models

Geng, Jianhua; Zhang, Bingwen; Huie, Lauren; Lai, Lifeng

doi:10.1109/tsp.2019.2914893

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…The moments that the break occurs are defined as the CPs. One of the important studies that models the unknown CP dividing the regression line into two parts is presented in [43]. This method is insufficient because it is based on the knowledge that there is only one CP in the data.…”

Section: Related Workmentioning

confidence: 99%

Change Point Detection Methods for Locating Activations in Functional Neuronal Images

Candemir

Oğuz

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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The most common analysis for fMRI images is activation detection, in which the purpose is to find the locations in the brain that respond to specific functions, such as visual processing or motor functions by providing related stimuli as tasks in the experiment. On the other hand, it is also important to detect the instance the activation is triggered. One of the powerful techniques that can analyze the abnormal behavior of any data is change point (CP) analysis. We suggest that CP detection algorithms also can be used to locate the activations in functional magnetic resonance imaging (fMRI) sequences, as well. Our paper presents a two-fold innovative study in that respect. First, we propose to use CP detection algorithms to locate the activations in fMRI signals as a state-of-art topic. Furthermore, we propose and compare a set of change point analysis methods, a regression-based method (RBM), a statistical method (SM), and a mean difference of double sliding windows method (MDSW)) to locate such points. Second, we apply these methods to the fMRI signals, which are acquired from the real subjects, while they were performing fMRI tasks. Proposed methods were applied to three different fMRI experiments with a motor task, a visual task, and a linguistic task. The analysis shows that the methods find activations in accordance with established methods such as statistical parametric maps (SPM). The acquired up to 94 % results also show that the proposed methods can be used effectively to locate the activation times on fMRI time series.

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Section: Related Workmentioning

confidence: 99%

Change Point Detection Methods for Locating Activations in Functional Neuronal Images

Candemir

Oğuz

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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“…There are also works investigating non-i.i.d. data under some specific settings, e.g., multi-sensor slope change detection [28], linear regression models [63], [221], generalized autoregressive conditional heteroskedasticity (GARCH) models [22], non-stationary time series [42], general stochastic models [195], [200], and hidden Markov models [62]. We refer to [196] for more recent developments on this topic.…”

Section: Generalizations and Extensions A General Asymptotic Theory F...mentioning

confidence: 99%

Sequential (Quickest) Change Detection: Classical Results and New Directions

Xie

Zou

Xie

et al. 2021

Preprint

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Online detection of changes in stochastic systems, referred to as sequential change detection or quickest change detection, is an important research topic in statistics, signal processing, and information theory, and has a wide range of applications. This survey starts with the basics of sequential change detection, and then moves on to generalizations and extensions of sequential change detection theory and methods. We also discuss some new dimensions that emerge at the intersection of sequential change detection with other areas, along with a selection of modern applications and remarks on open questions.

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“…We emphasize that Assumption (A1) was also considered for expectile models for example by Zhao et al (2018) and Ciuperca (2020), while Assumption (A2) is standard for linear models to ensure the identifiability of the coefficients (see for example Zou (2006), Geng et al (2019), Liao et al (2019)). Moreover, the errors (ε i ) 1 i m+Tm of models (2.1) and (2.2) will be assumed to be of the same distribution, not necessarily symmetrical.…”

Section: Preliminaries and Modelsmentioning

confidence: 99%

“…In order to locate the change-point in a multivariate data with heavy-tailed distribution, Liu et al (2019) propose a tail adaptive approach. Moreover, in order to detect in real-time an abrupt change in linear regression models, Geng et al (2019) propose a novel algorithm, in Bayesian and non-Bayesian formulation. Even if it is not the CUSUM method that is used, we consider it important to cite Horváth and Rice (2019)'s paper where a linear factor model is considered and where the dimension of the factors and the sample size tend jointly to infinity.…”

Section: Introductionmentioning

confidence: 99%

Real-time detection of a change-point in a linear expectile model

Ciuperca¹

2020

Preprint

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In the present paper we address the real-time detection problem of a change-point in the coefficients of a linear model with the possibility that the model errors are asymmetrical and that the explanatory variables number is large. We build test statistics based on the cumulative sum (CUSUM) of the expectile function derivatives calculated on the residuals obtained by the expectile and adaptive LASSO expectile estimation methods. The asymptotic distribution of these statistics are obtained under the hypothesis that the model does not change. Moreover, we prove that they diverge when the model changes at an unknown observation. The asymptotic study of the test statistics under these two hypotheses allows us to find the asymptotic critical region and the stopping time, that is the observation where the model will change. The empirical performance is investigated by a comparative simulation study with other statistics of CUSUM type. Two examples on real data are also presented to demonstrate its interest in practice.

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Online Change-Point Detection of Linear Regression Models

Cited by 9 publications

References 33 publications

Change Point Detection Methods for Locating Activations in Functional Neuronal Images

Change Point Detection Methods for Locating Activations in Functional Neuronal Images

Sequential (Quickest) Change Detection: Classical Results and New Directions

Real-time detection of a change-point in a linear expectile model

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