Variance change point detection for fractional Brownian motion based on the likelihood ratio test

Kucharczyk, Daniel; Wyłomańska, Agnieszka; Sikora, Grzegorz

doi:10.1016/j.physa.2017.08.134

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…Change point problem started in control theory, and it dates back to 1950s decade [5,6] where the existence of a single change point was tested. In order to detect a single change point, the first case analyzed dealt with independent random variables considering parametric methodologies [7][8][9][10][11][12][13]. To improve criteria for identifying and estimating change points, Bayesian and nonparametric methodologies have been used [7,8,[14][15][16][17][18][19][20][21].…”

Section: Parametric Methodologies For Detecting Change Pointsmentioning

confidence: 99%

Parametric Methodologies for Detecting Changes in Maximum Temperature of Tlaxco, Tlaxcala, México

Cortés

Hernández

Guevara

et al. 2019

Journal of Probability and Statistics

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In this paper, comparison results of parametric methodologies of change points, applied to maximum temperature records from the municipality of Tlaxco, Tlaxcala, México, are presented. Methodologies considered are likelihood ratio test, score test, and binary segmentation (BS), pruned exact linear time (PELT), and segment neighborhood (SN). In order to compare such methodologies, a quality analysis of the data was performed; in addition, lost data were estimated with linear regression, and finally, SARIMA models were adjusted.

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Section: Parametric Methodologies For Detecting Change Pointsmentioning

confidence: 99%

Parametric Methodologies for Detecting Changes in Maximum Temperature of Tlaxco, Tlaxcala, México

Cortés

Hernández

Guevara

et al. 2019

Journal of Probability and Statistics

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show abstract

“…The likelihood ratio method has been used for change point detection in Brownian motion. A statistical test was also suggested to identify the significance of a calculated critical point (Kucharczyk et al [8]). The change point detection technique in machine monitoring was suggested, which was based on two stages.…”

Section: Introductionmentioning

confidence: 99%

Change Point Detection for Airborne Particulate Matter (PM2.5, PM10) by Using the Bayesian Approach

Khan

Sarkar

2019

Mathematics

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Airborne particulate matter (PM) is a key air pollutant that affects human health adversely. Exposure to high concentrations of such particles may cause premature death, heart disease, respiratory problems, or reduced lung function. Previous work on particulate matter ( P M 2.5 and P M 10 ) was limited to specific areas. Therefore, more studies are required to investigate airborne particulate matter patterns due to their complex and varying properties, and their associated ( P M 10 and P M 2.5 ) concentrations and compositions to assess the numerical productivity of pollution control programs for air quality. Consequently, to control particulate matter pollution and to make effective plans for counter measurement, it is important to measure the efficiency and efficacy of policies applied by the Ministry of Environment. The primary purpose of this research is to construct a simulation model for the identification of a change point in particulate matter ( P M 2.5 and P M 10 ) concentration, and if it occurs in different areas of the world. The methodology is based on the Bayesian approach for the analysis of different data structures and a likelihood ratio test is used to a detect change point at unknown time (k). Real time data of particulate matter concentrations at different locations has been used for numerical verification. The model parameters before change point ( θ ) and parameters after change point ( λ ) have been critically analyzed so that the proficiency and success of environmental policies for particulate matter ( P M 2.5 and P M 10 ) concentrations can be evaluated. The main reason for using different areas is their considerably different features, i.e., environment, population densities, and transportation vehicle densities. Consequently, this study also provides insights about how well this suggested model could perform in different areas.

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Bayesian autoregressive online change-point detection with time-varying parameters

Tsaknaki,

Lillo,

Mazzarisi

2025

Communications in Nonlinear Science and Numerical Simulation

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Variance change point detection for fractional Brownian motion based on the likelihood ratio test

Cited by 7 publications

References 42 publications

Parametric Methodologies for Detecting Changes in Maximum Temperature of Tlaxco, Tlaxcala, México

Parametric Methodologies for Detecting Changes in Maximum Temperature of Tlaxco, Tlaxcala, México

Change Point Detection for Airborne Particulate Matter (PM2.5, PM10) by Using the Bayesian Approach

Bayesian autoregressive online change-point detection with time-varying parameters

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