Change detection for uncertain autoregressive dynamic models through nonparametric estimation

Hilgert, Nadine; Verdier, Ghislain; Vila, Jean-Pierre

doi:10.1016/j.stamet.2016.08.003

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…On the basis of nonparametric estimation of unidentified elements, an innovative CUSUM-like scheme was recommended for change detection. This estimation method could also be updated online (Hilgert et al [12]). A new methodology, the Karhunen-Loeve expansions of the limit Gaussian processes, was suggested for change point test in the level of a series.…”

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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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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“…A statistical change point algorithm was proposed in which direct density ratio estimation technique was used for deviation measurement of nonparametric deviation estimation among time series samples through relative Pearson divergence variable data structures [10]. An innovative statistical approach for online change point detection was recommended in which estimation method could also be updated online [11]. An economic production quantity model with stochastic demand was developed for an imperfect production system [12].…”

Section: Introductionmentioning

confidence: 99%

Change Point Detection for Diversely Distributed Stochastic Processes Using a Probabilistic Method

Khan

Sarkar

2019

Inventions

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Unpredicted deviations in time series data are called change points. These unexpected changes indicate transitions between states. Change point detection is a valuable technique in modeling to estimate unanticipated property changes underlying time series data. It can be applied in different areas like climate change detection, human activity analysis, medical condition monitoring and speech and image analyses. Supervised and unsupervised techniques are equally used to identify changes in time series. Even though change point detection algorithms have improved considerably in recent years, several undefended challenges exist. Previous work on change point detection was limited to specific areas; therefore, more studies are required to investigate appropriate change point detection techniques applicable to any data distribution to assess the numerical productivity of any stochastic process. This research is primarily focused on the formulation of an innovative methodology for change point detection of diversely distributed stochastic processes using a probabilistic method with variable data structures. Bayesian inference and a likelihood ratio test are used to detect a change point at an unknown time (k). The likelihood of k is determined and used in the likelihood ratio test. Parameter change must be evaluated by critically analyzing the parameters expectations before and after a change point. Real-time data of particulate matter concentrations at different locations were used for numerical verification, due to diverse features, that is, environment, population densities and transportation vehicle densities. Therefore, this study provides an understanding of how well this recommended model could perform for different data structures.

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An empirical likelihood-based CUSUM for on-line model change detection

Verdier

2019

Communications in Statistics - Theory and Methods

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Change detection for uncertain autoregressive dynamic models through nonparametric estimation

Cited by 4 publications

References 39 publications

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

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

Change Point Detection for Diversely Distributed Stochastic Processes Using a Probabilistic Method

An empirical likelihood-based CUSUM for on-line model change detection

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