Consistency of the Model Order Change-Point Estimator for GARCH Models

In this paper, a randomised pseudolikelihood ratio change point estimator for GARCH model is presented. Derivation of a randomised change point estimator for the GARCH model and its consistency are given. Simulation results that support the validity of the estimator are also presented. It was observed that the randomised estimator outperforms the ordinary CUSUM of squares test, and it is optimal with large variance change ratios.

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Change Point Estimation in Panel Data without Boundary Issue

10.20944/preprints201612.0002.v1 ◽

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.

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

Bioinformatics ◽

10.1093/bioinformatics/btab685 ◽

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.

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Change Point Detection in GARCH Models for Voice Activity Detection

IEEE Transactions on Audio Speech and Language Processing ◽

10.1109/tasl.2008.922468 ◽

2008 ◽

Vol 16 (5) ◽

pp. 1038-1046 ◽

Cited By ~ 14

Author(s):

R. Tahmasbi ◽

S. Rezaei

Keyword(s):

Change Point ◽

Voice Activity Detection ◽

Change Point Detection ◽

Garch Models ◽

Activity Detection ◽

Point Detection ◽

Voice Activity

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

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2007.912663 ◽

2008 ◽

Vol 55 (2) ◽

pp. 453-467 ◽

Cited By ~ 24

Author(s):

Patricio S. La Rosa ◽

Arye Nehorai ◽

Hari Eswaran ◽

Curtis L. Lowery ◽

Hubert Preissl

Keyword(s):

Change Point ◽

Cluster Algorithm ◽

Point Estimator

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

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539305001719 ◽

2005 ◽

Vol 12 (02) ◽

pp. 95-110 ◽

Cited By ~ 17

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.

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