scholarly journals Some results on change-point detection in cross-sectional dependence of multivariate data with changes in marginal distributions

2016 ◽  
Vol 119 ◽  
pp. 45-54
Author(s):  
Tom Rohmer
Keyword(s):  
Change Point ◽  
Multivariate Data ◽  
Change Point Detection ◽  
Cross Sectional ◽  
Marginal Distributions ◽  
Cross Sectional Dependence ◽  
Point Detection

scholarly journals Multi-Stage Change Point Detection with Copula Conditional Distribution with PCA and Functional PCA

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1777
Author(s):  
Jong-Min Kim ◽  
Ning Wang ◽  
Yumin Liu
Keyword(s):  
Change Point ◽  
Manufacturing Industry ◽  
Supply And Demand ◽  
Multivariate Data ◽  
Change Point Detection ◽  
Conditional Distributions ◽  
Multi Stage ◽  
Functional Pca ◽  
Point Detection ◽  
Uncertainty Environment

A global uncertainty environment, such as the COVID-19 pandemic, has affected the manufacturing industry severely in terms of supply and demand balancing. So, it is common that one stage statistical process control (SPC) chart affects the next-stage SPC chart. It is our research objective to consider a conditional case for the multi-stage multivariate change point detection (CPD) model for highly correlated multivariate data via copula conditional distributions with principal component analysis (PCA) and functional PCA (FPCA). First of all, we review the current available multivariate CPD models, which are the energy test-based control chart (ETCC) and the nonparametric multivariate change point model (NPMVCP). We extend the current available CPD models to the conditional multi-stage multivariate CPD model via copula conditional distributions with PCA for linear normal multivariate data and FPCA for nonlinear non-normal multivariate data.

Testing constant cross-sectional dependence with time-varying marginal distributions in parametric models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Matthias Kaldorf ◽  
Dominik Wied
Keyword(s):  
Change Point ◽  
Equity Returns ◽  
Parametric Models ◽  
Test Statistics ◽  
Cross Sectional ◽  
Marginal Distributions ◽  
Point Estimator ◽  
Cross Sectional Dependence ◽  
European Banks ◽  
The Stability

AbstractThis paper proposes parametric two-step procedures for assessing the stability of cross-sectional dependency measures in the presence of potential breaks in the marginal distributions. The procedures are based on formerly proposed sup-LR tests in which restricted and unrestricted likelihood functions are compared with each other. First, we show theoretically that standard asymptotics do not hold in this situation. We propose a suitable bootstrap scheme and derive test statistics in different commonly used settings. The properties of the test statistics and precision of the associated change-point estimator are analysed and compared with existing non-parametric methods in various Monte Carlo simulations. These studies reveal advantages in test power for higher-dimensional data and an almost uniform superiority of the sup-LR test in terms of precision of the change-point estimator. We then apply this method to equity returns of European banks during the financial crisis of 2008.

scholarly journals Change point detection for compositional multivariate data

2021 ◽  
Author(s):  
Prabuchandran K. J. ◽  
Nitin Singh ◽  
Pankaj Dayama ◽  
Ashutosh Agarwal ◽  
Vinayaka Pandit
Keyword(s):  
Change Point ◽  
Multivariate Data ◽  
Change Point Detection ◽  
Point Detection

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.

Sign in / Sign up

Export Citation Format

Share Document