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

2018 ◽  
Vol 490 ◽  
pp. 439-450 ◽  
Author(s):  
Daniel Kucharczyk ◽  
Agnieszka Wyłomańska ◽  
Grzegorz Sikora
Keyword(s):  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Change Point ◽  
Change Point Detection ◽  
Ratio Test ◽  
Variance Change ◽  
Point Detection ◽  
Variance Change Point

Variance change-point detection in panel data models

2015 ◽  
Vol 126 ◽  
pp. 140-143 ◽  
Author(s):  
Fuxiao Li ◽  
Zheng Tian ◽  
Yanting Xiao ◽  
Zhanshou Chen
Keyword(s):  
Panel Data ◽  
Change Point ◽  
Data Models ◽  
Change Point Detection ◽  
Panel Data Models ◽  
Variance Change ◽  
Point Detection ◽  
Variance Change Point

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

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 42
Author(s):  
Muhammad Rizwan Khan ◽  
Biswajit Sarkar
Keyword(s):  
Time Series ◽  
Likelihood Ratio Test ◽  
Data Structures ◽  
Change Point ◽  
Time Series Data ◽  
Probabilistic Method ◽  
Change Point Detection ◽  
Series Data ◽  
Ratio Test ◽  
Point Detection

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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