• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1305-1310
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• 2010

In this paper, we consider a change point test for stochastic volatility models. By considering the relation between moments of the logarithms of squared returns and the parameters, we construct the cusum test to detect changes of the parameters. We also carry out a simulation study and verify that the proposed test is more powerful than the cusum test proposed by Kokoszka and Leipus (2000).

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.287-293
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• 2003

In this paper we consider the problem of parameter change based on the cusum test proposed by Lee et al. (2003). The cusum test statistic is constructed utilizing the estimator minimizing density-based divergence measures. It is shown that under regularity conditions, the test statistic has the limiting distribution of the sup of standard Brownian bridge. Simulation results demonstrate that the cusum test is robust when there arc outliers.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.476-481
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• 2015

We have parallelized the cumulative sum (CUSUM) test of NIST's statistical random number test suite in a CUDA environment. Storing random walks in an array instead of in scalar variables eliminates data dependence. The change in data structure makes it possible to apply parallel scans, scatters, and reductions at each stage of the test. In addition, serial data exchanges between CPU and GPU are removed by migrating CPU's tasks to GPU. Finally we have optimized global memory accesses. The overall speedup is 23 times over the sequential version. Our results contribute to improving security of random numbers for cryptographic keys as well as reducing the time for evaluation of randomness.

• Journal of Korean Society for Quality Management
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• v.9 no.2
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• pp.26-30
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• 1981

A typical industrial data - monitoring scheme often requires trend detection Trend detection can be accomplished in many ways. Common statistical methods are the sign test, the run test, and the trend test. Graphical methods include various smoothing schemes and the cusum. The cusum has established itself as an efficient method of detecting changes in the mean level of a process being monitored. The cusum requires a "target value" with which the raw data are compared. At production start - up it is often difficult to designate the target value. This paper offers a means of initiating the cusum technique without a target value.

• The Korean Journal of Applied Statistics
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• v.3 no.1
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• pp.105-120
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• 1990

In this paper, a sequential test procedure is defined by using cumulative sum (CUSUM) of statistics. The properties as well as the efficiency of the CUSUM test are studied in comparison with the sequential probability ratio test (SPRT). It was shown that, the operating characteristic function and the average sample numbrer can be derived by Wald and Wiener process approximations. Also it was shown that the statistics used in the CUSUM test is determined to provide asymtotically equivalent efficiency compared to the SPRT. The efficiency of the CUSUM test and the SPRT are cpmpared by an example for some limited number of cases in the exponential distribution.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1565-1572
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• 2015

In this paper, we analyze the polio incidence data based on the Poisson autoregressive models, focusing particularly on change-point detection. Since the data include some strongly deviating observations, we employ the robust cumulative sum (CUSUM) test proposed by Kang and Song (2015) to perform the test for parameter change. Contrary to the result of Kang and Lee (2014), our data analysis indicates that there is no significant change in the case of the CUSUM test with strong robustness and the same result is obtained after ridding the polio data of outliers. We additionally consider the comparison of the forecasting performance. All the results demonstrate that the robust CUSUM test performs adequately in the presence of seemingly outliers.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.135-138
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• 2005

In this paper, we consider the problem of testing for parameter change in time series models based on a cusum of squares. Although the test procedure is well-established for the mean and variance in time series models, a general parameter case was not discussed in literatures. Therefore, here we develop the cusum of squares type test for parameter change in a more general framework. As an example, we consider the change of the parameters in an RCA(1) model. Simulation results are reported for illustration.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.291-305
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• 2014

Persistence is one of the typical characteristics appearing in the volatility of financial time series. According to the recent researches, the volatility persistence may be due to either volatility shifts or long-range dependence. In this paper, we consider residual-based CUSUM tests to distinguish volatility persistence, long-range dependence and volatility shifts in GARCH models. It is observed that this test procedure achieve reasonable powers without a size distortion. Moreover, we employ AIC and BIC criteria to estimate the change points and the number of change points in volatility. We demonstrate the superiority of residual-based CUSUM tests on various Monte Carlo simulations and empirical data analysis.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.367-382
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• 2017

We consider an infinite-order long-memory heterogeneous autoregressive (HAR) model, which is motivated by a long-memory property of realized volatilities (RVs), as an extension of the finite order HAR-RV model. We develop bootstrap tests for structural mean or variance changes in the infinite-order HAR model via stationary bootstrapping. A functional central limit theorem is proved for stationary bootstrap sample, which enables us to develop stationary bootstrap cumulative sum (CUSUM) tests: a bootstrap test for mean break and a bootstrap test for variance break. Consistencies of the bootstrap null distributions of the CUSUM tests are proved. Consistencies of the bootstrap CUSUM tests are also proved under alternative hypotheses of mean or variance changes. A Monte-Carlo simulation shows that stationary bootstrapping improves the sizes of existing tests.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.187-199
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• 2003

We deal with the problem of testing a sequence of independent normal random variables with constant, known or unknown, variance for no change in mean versus alternatives with a single change-point. Various tests based on the likelihood ratio and recursive residuals, score statistics and cusums are studied. Proposed tests are modified version of Buckley's cusum statistics. A comparison study of various change-point test statistics is done by Monte Carlo simulation with S-plus software.