scholarly journals Exact Posterior Distributions over the Segmentation Space and Model Selection for Multiple Change-Point Detection Problems

2010 ◽  
pp. 557-564
Author(s):  
G. Rigaill ◽  
E. Lebarbier ◽  
S. Robin
Keyword(s):  
Model Selection ◽  
Change Point ◽  
Change Point Detection ◽  
Posterior Distributions ◽  
Selection For ◽  
Point Detection

scholarly journals Local Statistic With Dynamic Vertex Selection for Change-Point Detection in Stochastic Block Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 127954-127967
Author(s):  
Yanping Zhao ◽  
Bo Wang ◽  
Mingan Luan ◽  
Fengye Hu
Keyword(s):  
Change Point ◽  
Change Point Detection ◽  
Selection For ◽  
Point Detection

scholarly journals Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection—rejoinder

2020 ◽  
Vol 49 (4) ◽  
pp. 1099-1105 ◽  
Author(s):  
Piotr Fryzlewicz
Keyword(s):  
Model Selection ◽  
Change Point ◽  
Complete Solution ◽  
Change Point Detection ◽  
Change Points ◽  
Solution Path ◽  
Frequent Change ◽  
Nested Models ◽  
Binary Segmentation ◽  
Point Detection

AbstractMany existing procedures for detecting multiple change-points in data sequences fail in frequent-change-point scenarios. This article proposes a new change-point detection methodology designed to work well in both infrequent and frequent change-point settings. It is made up of two ingredients: one is “Wild Binary Segmentation 2” (WBS2), a recursive algorithm for producing what we call a ‘complete’ solution path to the change-point detection problem, i.e. a sequence of estimated nested models containing $$0, \ldots , T-1$$ 0 , … , T - 1 change-points, where T is the data length. The other ingredient is a new model selection procedure, referred to as “Steepest Drop to Low Levels” (SDLL). The SDLL criterion acts on the WBS2 solution path, and, unlike many existing model selection procedures for change-point problems, it is not penalty-based, and only uses thresholding as a certain discrete secondary check. The resulting WBS2.SDLL procedure, combining both ingredients, is shown to be consistent, and to significantly outperform the competition in the frequent change-point scenarios tested. WBS2.SDLL is fast, easy to code and does not require the choice of a window or span parameter.

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%.

Sign in / Sign up

Export Citation Format

Share Document