Detection of Uterine MMG Contractions Using a Multiple Change Point Estimator and the K-Means Cluster Algorithm

2008 ◽  
Vol 55 (2) ◽  
pp. 453-467 ◽  
Author(s):  
Patricio S. La Rosa ◽  
Arye Nehorai ◽  
Hari Eswaran ◽  
Curtis L. Lowery ◽  
Hubert Preissl
Keyword(s):  
Change Point ◽  
Cluster Algorithm ◽  
Point Estimator

Detection of uterine MMG contractions using a multiple change-point estimator and K-means cluster algorithm

2007 ◽  
Vol 1300 ◽  
pp. 745-748 ◽  
Author(s):  
P.S. La Rosa ◽  
A. Nehorai ◽  
H. Eswaran ◽  
C. Lowery ◽  
H. Preissl
Keyword(s):  
Change Point ◽  
Cluster Algorithm ◽  
Point Estimator

scholarly journals Change Point Estimation in Panel Data without Boundary Issue

2016 ◽  
Author(s):  
Barbora Peštová ◽  
Michal Pešta
Keyword(s):  
Panel Data ◽  
Change Point ◽  
Structural Breaks ◽  
Break Point ◽  
Point Estimation ◽  
The Novel ◽  
Structure Estimation ◽  
Change Point Estimation ◽  
Point Estimator ◽  
First Time

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

Efficient Change-Points Detection For Genomic Sequences Via Cumulative Segmented Regression

2021 ◽  
Author(s):  
Shengji Jia ◽  
Lei Shi
Keyword(s):  
Change Point ◽  
Serial Correlation ◽  
Copy Number Variations ◽  
Change Points ◽  
Supplementary Information ◽  
Genomic Sequences ◽  
Segmented Regression ◽  
Computationally Efficient ◽  
R Program ◽  
Point Estimator

Abstract Motivation Knowing the number and the exact locations of multiple change points in genomic sequences serves several biological needs. The cumulative segmented algorithm (cumSeg) has been recently proposed as a computationally efficient approach for multiple change-points detection, which is based on a simple transformation of data and provides results quite robust to model mis-specifications. However, the errors are also accumulated in the transformed model so that heteroscedasticity and serial correlation will show up, and thus the variations of the estimated change points will be quite different, while the locations of the change points should be of the same importance in the original genomic sequences. Results In this study, we develop two new change-points detection procedures in the framework of cumulative segmented regression. Simulations reveal that the proposed methods not only improve the efficiency of each change point estimator substantially but also provide the estimators with similar variations for all the change points. By applying these proposed algorithms to Coriel and SNP genotyping data, we illustrate their performance on detecting copy number variations. Supplementary information The proposed algorithms are implemented in R program and are available at Bioinformatics online.

ESTIMATION OF THE CHANGE POINT OF THE PROCESS FRACTION NONCONFORMING IN SPC APPLICATIONS

2005 ◽  
Vol 12 (02) ◽  
pp. 95-110 ◽  
Author(s):  
MARCUS B. PERRY ◽  
JOSEPH J. PIGNATIELLO
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Control Chart ◽  
Change Point ◽  
Control Charts ◽  
Process Change ◽  
Likelihood Estimator ◽  
Ewma Control Chart ◽  
Point Estimator ◽  
Point Estimators

Knowing when a process has changed would simplify the search for and identification of the special cause. In this paper, we compare the maximum likelihood estimator (MLE) of the process change point (that is, when the process changed) to built-in change point estimators from binomial CUSUM and EWMA control charts. We conclude that it is better to use the maximum likelihood change point estimator when a CUSUM or EWMA control chart signals a change in the process fraction nonconforming. The results show that the MLE provides process engineers with an accurate and useful estimate of the last subgroup from the unchanged process.

On the Inconsistency of the Change-Point Estimator for the NE Family

Metrika ◽  
2006 ◽  
Vol 63 (3) ◽  
pp. 309-315 ◽  
Author(s):  
Venkata K. Jandhyala ◽  
Stergios B. Fotopoulos ◽  
Douglas M. Hawkins
Keyword(s):  
Change Point ◽  
Point Estimator

scholarly journals Consistency of the <i>φ</i>-Divergence Based Change Point Estimator

2020 ◽  
Vol 10 (05) ◽  
pp. 832-849
Author(s):  
Mwelu Susan ◽  
Anthony G. Waititu ◽  
Peter N. Mwita ◽  
Charity Wamwea
Keyword(s):  
Change Point ◽  
Point Estimator

scholarly journals Convergence of a Randomised Change Point Estimator in GARCH Models

2021 ◽  
Vol 11 (02) ◽  
pp. 234-245
Author(s):  
George Awiakye-Marfo ◽  
Joseph Mung’atu ◽  
Patrick Weke
Keyword(s):  
Change Point ◽  
Garch Models ◽  
Point Estimator

Asymptotic distribution of the jump change-point estimator

2012 ◽  
Vol 33 (3) ◽  
pp. 429-436
Author(s):  
Changchun Tan ◽  
Huifang Niu ◽  
Baiqi Miao
Keyword(s):  
Asymptotic Distribution ◽  
Change Point ◽  
Point Estimator

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.

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