scholarly journals Robust Change Point Test for General Integer-Valued Time Series Models Based on Density Power Divergence

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 493 ◽  
Author(s):  
Byungsoo Kim ◽  
Sangyeol Lee
Keyword(s):  
Time Series ◽  
Change Point ◽  
Brownian Bridge ◽  
Null Distribution ◽  
Regularity Conditions ◽  
Time Series Models ◽  
Density Power Divergence ◽  
Change Point Test ◽  
Point Test ◽  
Power Divergence

In this study, we consider the problem of testing for a parameter change in general integer-valued time series models whose conditional distribution belongs to the one-parameter exponential family when the data are contaminated by outliers. In particular, we use a robust change point test based on density power divergence (DPD) as the objective function of the minimum density power divergence estimator (MDPDE). The results show that under regularity conditions, the limiting null distribution of the DPD-based test is a function of a Brownian bridge. Monte Carlo simulations are conducted to evaluate the performance of the proposed test and show that the test inherits the robust properties of the MDPDE and DPD. Lastly, we demonstrate the proposed test using a real data analysis of the return times of extreme events related to Goldman Sachs Group stock.

Can pitcher performance reveal game fixing in baseball? Developing a forensic analysis detection procedure

2018 ◽  
Vol 46 (2) ◽  
pp. 259-271
Author(s):  
Shun-Chuan Chang
Keyword(s):  
Behavior Analysis ◽  
Applied Behavior Analysis ◽  
Change Point ◽  
Textual Analysis ◽  
Forensic Analysis ◽  
Professional Baseball ◽  
Difference In Differences ◽  
Change Point Test ◽  
Sports Ethics ◽  
Point Test

Gambling and game-fixing scandals have loomed over the international baseball world and a lack of sports ethics in baseball may lead to many problems. In this study I conducted a textual analysis of reports by prosecutors regarding a pitcher who was investigated but not indicted in 2009 after allegations of game fixing. Drawing upon the statistical records of the season's games for the pitcher that were contained in the prosecutor's reports and game-by-game records for each Chinese Professional Baseball League pitcher in the 2009 regular season, I used the change-point test and difference-in-differences techniques to identify anomalies in the pitcher's play. The results I obtained support information contained in the prosecutors' reports regarding the pitcher's actions. My model is confirmed as an appropriate method of applied behavior analysis for detecting corruption in baseball pitching performance.

scholarly journals Consistent and powerful graph-based change-point test for high-dimensional data

2017 ◽  
Vol 114 (15) ◽  
pp. 3873-3878 ◽  
Author(s):  
Xiaoping Shi ◽  
Yuehua Wu ◽  
Calyampudi Radhakrishna Rao
Keyword(s):  
Change Point ◽  
Hamiltonian Path ◽  
Point Estimation ◽  
Change Point Detection ◽  
High Dimensional ◽  
Simulation Studies ◽  
Change Point Estimation ◽  
Change Point Test ◽  
Point Test ◽  
Point Detection

A change-point detection is proposed by using a Bayesian-type statistic based on the shortest Hamiltonian path, and the change-point is estimated by using ratio cut. A permutation procedure is applied to approximate the significance of Bayesian-type statistics. The change-point test is proven to be consistent, and an error probability in change-point estimation is provided. The test is very powerful against alternatives with a shift in variance and is accurate in change-point estimation, as shown in simulation studies. Its applicability in tracking cell division is illustrated.

scholarly journals A New Class of Change Point Test Statistics of Rényi Type

2019 ◽  
Vol 38 (3) ◽  
pp. 570-579 ◽  
Author(s):  
Lajos Horváth ◽  
Curtis Miller ◽  
Gregory Rice
Keyword(s):  
Change Point ◽  
Test Statistics ◽  
New Class ◽  
Change Point Test ◽  
Point Test

MINIMUM DISTANCE ESTIMATION OF NONSTATIONARY TIME SERIES MODELS

2002 ◽  
Vol 18 (6) ◽  
pp. 1385-1407 ◽  
Author(s):  
Hyungsik Roger Moon ◽  
Frank Schorfheide
Keyword(s):  
Time Series ◽  
Minimum Distance ◽  
Goodness Of Fit ◽  
Distance Estimation ◽  
Nonstationary Time Series ◽  
Regularity Conditions ◽  
Time Series Models ◽  
Goodness Of Fit Test ◽  
Restricted Parameter Space ◽  
Income Model

This paper analyzes the limit distribution of minimum distance (MD) estimators for nonstationary time series models that involve nonlinear parameter restrictions. A rotation for the restricted parameter space is constructed to separate the components of the MD estimator that converge at different rates. We derive regularity conditions for the restriction function that are easier to verify than the stochastic equicontinuity conditions that arise from direct estimation of the restricted parameters. The sequence of matrices that is used to weigh the discrepancy between the unrestricted estimates and the restriction function is allowed to have a stochastic limit. For MD estimators based on unrestricted estimators with a mixed normal asymptotic distribution the optimal weight matrix is derived and a goodness-of-fit test is proposed. Our estimation theory is illustrated in the context of a permanent-income model and a present-value model.

scholarly journals Change Point Test for the Conditional Mean of Time Series of Counts Based on Support Vector Regression

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 433
Author(s):  
Sangyeol Lee ◽  
Sangjo Lee
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Stock Prices ◽  
Change Point ◽  
Real Data ◽  
Support Vector ◽  
Time Series Of Counts ◽  
Conditional Mean ◽  
Change Point Test ◽  
Scope Of Application

This study considers support vector regression (SVR) and twin SVR (TSVR) for the time series of counts, wherein the hyper parameters are tuned using the particle swarm optimization (PSO) method. For prediction, we employ the framework of integer-valued generalized autoregressive conditional heteroskedasticity (INGARCH) models. As an application, we consider change point problems, using the cumulative sum (CUSUM) test based on the residuals obtained from the PSO-SVR and PSO-TSVR methods. We conduct Monte Carlo simulation experiments to illustrate the methods’ validity with various linear and nonlinear INGARCH models. Subsequently, a real data analysis, with the return times of extreme events constructed based on the daily log-returns of Goldman Sachs stock prices, is conducted to exhibit its scope of application.

scholarly journals Consistent and powerful non-Euclidean graph-based change-point test with applications to segmenting random interfered video data

2018 ◽  
Vol 115 (23) ◽  
pp. 5914-5919 ◽  
Author(s):  
Xiaoping Shi ◽  
Yuehua Wu ◽  
Calyampudi Radhakrishna Rao
Keyword(s):  
Change Point ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Hamiltonian Path ◽  
Video Data ◽  
Change Point Detection ◽  
Change Point Test ◽  
Point Test ◽  
Point Detection

The change-point detection has been carried out in terms of the Euclidean minimum spanning tree (MST) and shortest Hamiltonian path (SHP), with successful applications in the determination of authorship of a classic novel, the detection of change in a network over time, the detection of cell divisions, etc. However, these Euclidean graph-based tests may fail if a dataset contains random interferences. To solve this problem, we present a powerful non-Euclidean SHP-based test, which is consistent and distribution-free. The simulation shows that the test is more powerful than both Euclidean MST- and SHP-based tests and the non-Euclidean MST-based test. Its applicability in detecting both landing and departure times in video data of bees’ flower visits is illustrated.

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