Assessing the performance of change-point detection based on the SIC procedure for non Gaussian and correlated data - A simulation study

2013 ◽  
Author(s):  
A. Manuela Gonçalves ◽  
Marco Costa ◽  
Lara Teixeira
Keyword(s):  
Simulation Study ◽  
Change Point ◽  
Correlated Data ◽  
Change Point Detection ◽  
Non Gaussian ◽  
Point Detection

scholarly journals Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity

2018 ◽  
Vol 119 (4) ◽  
pp. 1394-1410 ◽  
Author(s):  
Sile Hu ◽  
Qiaosheng Zhang ◽  
Jing Wang ◽  
Zhe Chen
Keyword(s):  
Real Time ◽  
Spike Activity ◽  
Change Point ◽  
Particle Filtering ◽  
Jump Process ◽  
Change Point Detection ◽  
Stochastic Jump ◽  
Abrupt Changes ◽  
Non Gaussian ◽  
Point Detection

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

scholarly journals Change Point Detection of Sustainable Periods of Economic Systems Under the Robust Control

2019 ◽  
Vol 26 (2) ◽  
pp. 27-36
Author(s):  
S. E. Khrushchev ◽  
M. A. Alekseev ◽  
O. M. Logachova
Keyword(s):  
Robust Control ◽  
Change Point ◽  
Regression Models ◽  
Small Volume ◽  
Statistical Modelling ◽  
Change Point Detection ◽  
Economic Systems ◽  
Non Gaussian ◽  
Point Detection ◽  
And Control

This article addresses the potential of mathematical and statistical modelling the change point detection in economic systems on the example of UC «RUSAL». Change point prediction of stable or quasi-stable periods of economic systems is necessary for the operational changing of a strategy, tactics and control of the considered economic system. It solves one of the robust control problems, the purpose of which is the synthesis of the regulator that can provide the preservation of output variables of the system within the robust limit for all types of membership functions and the uncertainty of the input data.The developed algorithm is based on the study of the behavior of residuals of regression models by the observed series of the dynamics of some exponent (as a benchmark was chosen the price of ordinary share). This algorithm is applicable for small volume samples, which, as a rule, are the series of dynamics of exponents of economic systems and also, in the study of non-Gaussian observational models.

Understanding the CoVID-19 pandemic Curve through statistical approach (Preprint)

2020 ◽  
Author(s):  
Ibrar Ul Hassan Akhtar
Keyword(s):  
Probability Density ◽  
Change Point ◽  
Statistical Approach ◽  
Sudden Change ◽  
Global Scale ◽  
Change Point Detection ◽  
Segment Length ◽  
Infected Population ◽  
The Mean ◽  
Point Detection

UNSTRUCTURED Current research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 ‒ 9.89 and 0 ‒ 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of -78.8 to 49.0%.

scholarly journals Change point detection with multiple alternatives reveals parallel evaluation of the same stream of evidence along distinct timescales

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexa Booras ◽  
Tanner Stevenson ◽  
Connor N. McCormack ◽  
Marie E. Rhoads ◽  
Timothy D. Hanks
Keyword(s):  
Change Point ◽  
Neural Mechanism ◽  
Detection Task ◽  
Change Point Detection ◽  
Model Free ◽  
Evidence Evaluation ◽  
Parallel Evaluation ◽  
Point Detection ◽  
Improved Accuracy

AbstractIn order to behave appropriately in a rapidly changing world, individuals must be able to detect when changes occur in that environment. However, at any given moment, there are a multitude of potential changes of behavioral significance that could occur. Here we investigate how knowledge about the space of possible changes affects human change point detection. We used a stochastic auditory change point detection task that allowed model-free and model-based characterization of the decision process people employ. We found that subjects can simultaneously apply distinct timescales of evidence evaluation to the same stream of evidence when there are multiple types of changes possible. Informative cues that specified the nature of the change led to improved accuracy for change point detection through mechanisms involving both the timescales of evidence evaluation and adjustments of decision bounds. These results establish three important capacities of information processing for decision making that any proposed neural mechanism of evidence evaluation must be able to support: the ability to simultaneously employ multiple timescales of evidence evaluation, the ability to rapidly adjust those timescales, and the ability to modify the amount of information required to make a decision in the context of flexible timescales.

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