• 제목/요약/키워드: CUSUM statistic

검색결과 29건 처리시간 0.024초

Bootstrap-Based Test for Volatility Shifts in GARCH against Long-Range Dependence

  • Wang, Yu;Park, Cheolwoo;Lee, Taewook
    • Communications for Statistical Applications and Methods
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    • 제22권5호
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    • pp.495-506
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    • 2015
  • Volatility is a variation measure in finance for returns of a financial instrument over time. GARCH models have been a popular tool to analyze volatility of financial time series data since Bollerslev (1986) and it is said that volatility is highly persistent when the sum of the estimated coefficients of the squared lagged returns and the lagged conditional variance terms in GARCH models is close to 1. Regarding persistence, numerous methods have been proposed to test if such persistency is due to volatility shifts in the market or natural fluctuation explained by stationary long-range dependence (LRD). Recently, Lee et al. (2015) proposed a residual-based cumulative sum (CUSUM) test statistic to test volatility shifts in GARCH models against LRD. We propose a bootstrap-based approach for the residual-based test and compare the sizes and powers of our bootstrap-based CUSUM test with the one in Lee et al. (2015) through simulation studies.

Comparison of EWMA and CUSUM Charts with Variable Sampling Intervals for Monitoring Variance-Covariance Matrix

  • Chang, Duk-Joon
    • 통합자연과학논문집
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    • 제13권4호
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    • pp.152-157
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    • 2020
  • To monitor all elements simultaneously of variance-covariance matrix Σ of several correlated quality characteristics under multivariate normal process Np($\underline{\mu}$, Σ), multivariate exponentially weighted moving average (EWMA) chart and cumulative sum (CUSUM) chart are considered and compared. Numerical performances of the considered variable sampling interval (VSI) charts are evaluated using average run length (ARL), average time to signal (ATS), average number of switches (ANSW) to signal, and the probability of switch Pr(switch) between two sampling interval d1 and d2 where d1 < d2. For small or moderate changes of Σ, the performances of multivariate EWMA chart is approximately equivalent to that of multivariate CUSUM chart.

SPRT를 기반으로 하는 누적합 스테간 분석을 이용한 은닉메시지 감지기법 (Detecting Hidden Messages Using CUSUM Steganalysis based on SPRT)

  • 지선수
    • 한국산업정보학회논문지
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    • 제15권3호
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    • pp.51-57
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    • 2010
  • 스테가노그래피는 이미지의 외적인 면에서 미세한 변화를 가진 디지털 이미지에 자료를 은닉하기 위해 사용된다. 은닉이미지가 의심되는 스테고 신호 분석에서 개선된 통계량을 이용하여 갑작스러운 변화를 신속, 정확하게 감지하는 기법의 개발이 필요하다. 이 논문에서는 축차적인 스테가노그래피에서 은닉된 메시지를 감지하고 그 위치를 찾아내는 방법을 제시한다. 즉, 검사하는 이미지에 은닉메시지의 존재 유무를 결정하고 그 위치를 찾아낼 때까지 CUSUM-SPRT 스테간 분석을 기반으로 하는 통계적 검정을 반복한다. 논문에서 일반화된 수식을 위해 개선된 $S^{t^*}_j$를 이용한 통계량 $g_t$를 사용한다.

가변 샘플링 간격(VSI)을 갖는 적응형 이동평균 (A-MA) 관리도 (An Adaptive Moving Average (A-MA) Control Chart with Variable Sampling Intervals (VSI))

  • 임태진
    • 대한산업공학회지
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    • 제33권4호
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    • pp.457-468
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    • 2007
  • This paper proposes an adaptive moving average (A-MA) control chart with variable sampling intervals (VSI) for detecting shifts in the process mean. The basic idea of the VSI A-MA chart is to adjust sampling intervals as well as to accumulate previous samples selectively in order to increase the sensitivity. The VSI A-MA chart employs a threshold limit to determine whether or not to increase sampling rate as well as to accumulate previous samples. If a standardized control statistic falls outside the threshold limit, the next sample is taken with higher sampling rate and is accumulated to calculate the next control statistic. If the control statistic falls within the threshold limit, the next sample is taken with lower sampling rate and only the sample is used to get the control statistic. The VSI A-MA chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L-consecutive control statistics fall outside the threshold limit. The control length L is introduced to prevent small mean shifts from being undetected for a long period. A Markov chain model is employed to investigate the VSI A-MA sampling process. Formulae related to the steady state average time-to signal (ATS) for an in-control state and out-of-control state are derived in closed forms. A statistical design procedure for the VSI A-MA chart is proposed. Comparative studies show that the proposed VSI A-MA chart is uniformly superior to the adaptive Cumulative sum (CUSUM) chart and to the Exponentially Weighted Moving Average (EWMA) chart, and is comparable to the variable sampling size (VSS) VSI EWMA chart with respect to the ATS performance.

A Study on UBM Method Detecting Mean Shift in Autocorrelated Process Control

  • Jun, Sang-Pyo
    • 한국컴퓨터정보학회논문지
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    • 제25권12호
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    • pp.187-194
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    • 2020
  • 오늘날 반도체나 석유 화학 공정과 같이 프로세스 중심의 산업에서는 관측된 자료들 사이에 자기 상관이 존재한다. 자기상관이 존재하는 공정에 대한 관리 방법으로는 관측치를 이용하여 뱃치 평균이 독립에 가까워지도록 뱃치를 구성하여 관리하거나, 관측치의 EWMA (지수 가중치 이동 평균) 통계량을 EWMA 관리도에 적용하는 방법등이 주로 사용되고 있다. 본 논문에서는 관찰치에 대한 관리 방법으로 일반적으로 사용되는 UBM 의 뱃치 크기를 결정하는 방법을 소개하고, ARL(평균 실행 길이)을 기반으로 최적 뱃치 크기를 정하는 방법과 그러한 뱃치 구성에서 공정의 표준 편차를 추정하는 방법을 제안 한다. 자기상관이 존재하는 공정에 대한 개선된 관리도를 제안하고자 한다.

관리도를 이용한 터널 시공현장 계측변위 분석 기법 개발 (A New Method for the Analysis of Measured Displacements during Tunnelling using Control Charts)

  • 임성빈;서용석
    • 지질공학
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    • 제19권3호
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    • pp.261-268
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    • 2009
  • 터널 굴착 중 수행되는 계측은 구성 지반의 거동을 가시화하여 안정성을 평가할 수 있는 중요한 정보를 제공한다. 일반적으로 계측은 터널 굴착면 형성 초기에 집중적으로 수행되며, 시간이 경과할수록 계측횟수를 줄여 나간다. 하지만 이러한 계측 기준 및 관리 지침은 획일적이며 뚜렷한 정량적 기준이 부족한 실정이다. 본 연구에서는 굴착 초기 발생하는 변위의 안정화 시점의 정량적 판단과 안정화 이후의 미세한 변위 변동 특성 분석 및 이상거동 평가를 위해 통계 관리도 기법을 적용하였다. 계측변위에 대한 이동범위(MR) 관리도 및 누적합(CUSUM) 관리도를 작성하여 변위 평가 가능성을 검토하였다.

Some Tsets for Variance Changes in Time Series with a Unit Root

  • Park, Young-J.;Cho, Sin-Sup
    • Communications for Statistical Applications and Methods
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    • 제4권1호
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    • pp.101-109
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    • 1997
  • For the detection on variance changes in the nonstationary time series with a unit root two types of test statistics are proposed, of which one is based on the cumulative sum of squares and the other is based on the likelihood ratio test. The properties of the cusum type test statistic are derived and the performance of two tests in small samples are compared through Monte Carlo study. It is ovserved that the test based on the cumulative sum of squares can detect a samll change in the variance faster than the one based on the likelihood ratio.

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Numerical Switching Performances of Cumulative Sum Chart for Dispersion Matrix

  • Chang, Duk-Joon
    • 통합자연과학논문집
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    • 제12권3호
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    • pp.78-84
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    • 2019
  • In many cases, the quality of a product is determined by several correlated quality variables. Control charts have been used for a long time widely to control the production process and to quickly detect the assignable causes that may produce any deterioration in the quality of a product. Numerical switching performances of multivariate cumulative sum control chart for simultaneous monitoring all components in the dispersion matrix ${\Sigma}$ under multivariate normal process $N_p({\underline{\mu}},{\Sigma})$ are considered. Numerical performances were evaluated for various shifts of the values of variances and/or correlation coefficients in ${\Sigma}$. Our computational results show that if one wants to quick detect the small shifts in a process, CUSUM control chart with small reference value k is more efficient than large k in terms of average run length (ARL), average time to signal (ATS), average number of switches (ANSW).

Change points detection for nonstationary multivariate time series

  • Yeonjoo Park;Hyeongjun Im;Yaeji Lim
    • Communications for Statistical Applications and Methods
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    • 제30권4호
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    • pp.369-388
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    • 2023
  • In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.