• Journal of the Korean Data and Information Science Society
• /
• 제27권2호
• /
• pp.523-530
• /
• 2016

Most classical control charts assume that processes are serially independent, and autocorrelation among variables makes them unreliable. To address this issue, a variety of statistical approaches has been employed to estimate the serial structure of the process. In this paper, we propose a multioutput least squares support vector regression and apply it to construct a residual multivariate cumulative sum control chart for detecting changes in the process mean vector. Numerical studies demonstrate that the proposed multioutput least squares support vector regression based control chart provides more satisfying results in detecting small shifts in the process mean vector.

• 응용통계연구
• /
• 제19권2호
• /
• pp.241-256
• /
• 2006

EDF에 근거한 $Cram{\acute{e}}r$-von Mises 통계량을 합교원리를 이용하여 다변량으로 일반화한다. 그리고 제안된 통계량의 귀무가설에서의 극한분포를 적절한 공분산 함수를 가진 가우스 과정의 적분의 형태로 표현하고 통계량의 근사적인 계산방법을 고려한다. 또한 실제 자료에 제안된 통계량을 적용해보고 여러가지 대립가설에서의 검정력을 유사한 통계량과 비교해 본다.

• Journal of the Korean Data and Information Science Society
• /
• 제24권4호
• /
• pp.919-929
• /
• 2013

We compared two basic methods, combine-accumulate method and accumulate-combine method, using the past quality information in multivariate quality control procedure for monitoring mean vector of multivariate normal process. When small or moderate shifts have occurred, accumulate-combine method yields smaller average run length (ARL) and average time to signal (ATS) than combine-accumulate method. On the other hand, we have found from our numerical results that combine-accumulate method has better performances in terms of switching behavior than accumulate-combine method. In industry, a quality engineer could select one of the two method under the comprehensive consideration about the required time to signal, switching behavior, and other physical factors in the production process.

• 한국수학교육학회지시리즈B:순수및응용수학
• /
• 제11권2호
• /
• pp.139-147
• /
• 2004

Let {<$\mathds{X}_t$} be an m-dimensional linear process of the form $\mathbb{X}_t\;=\sumA,\mathbb{Z}_{t-j}$ where {$\mathbb{Z}_t$} is a sequence of stationary m-dimensional negatively associated random vectors with $\mathbb{EZ}_t$ = $\mathbb{O}$ and $\mathbb{E}\parallel\mathbb{Z}_t\parallel^2$ < $\infty$. In this paper we prove the central limit theorems for multivariate linear processes generated by negatively associated random vectors.

• 통합자연과학논문집
• /
• 제5권3호
• /
• pp.151-156
• /
• 2012

Because of the equivalence between control chart procedures and hypothesis testing, we propose to use likelihood ratio test (LRT) statistic $Z_i^2$ as the multivariate control statistic for simultaneous monitoring means of the multivariate normal process. Properties and comparisons of the proposed control charts are explored and conducted for matched fixed sampling interval (FSI) and variable sampling interval (VSI) with two sampling interval charts. The result of numerical comparisons shows that EWMA chart with two sampling interval procedure is more efficient than the corresponding FSI chart for small or moderate changes. When large shift of the process has occurred, we also found that Shewhart chart is more efficient than EWMA chart.

• Communications for Statistical Applications and Methods
• /
• 제25권6호
• /
• pp.605-618
• /
• 2018

Risk management has been a crucial part of the daily operations of the financial industry over the past two decades. Value at Risk (VaR), a quantitative measure introduced by JP Morgan in 1995, is the most popular and simplest quantitative measure of risk. VaR has been widely applied to the risk evaluation over all types of financial activities, including portfolio management and asset allocation. This paper uses the implementations of multivariate GARCH models and copula methods to illustrate the performance of a one-day-ahead VaR prediction modeling process for high-dimensional portfolios. Many factors, such as the interaction among included assets, are included in the modeling process. Additionally, empirical data analyses and backtesting results are demonstrated through a rolling analysis, which help capture the instability of parameter estimates. We find that our way of modeling is relatively robust and flexible.

• Environmental Engineering Research
• /
• 제11권2호
• /
• pp.63-76
• /
• 2006

Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.

• Communications for Statistical Applications and Methods
• /
• 제2권1호
• /
• pp.201-208
• /
• 1995

In the last years there has been growing interest in concepts of positive dependence for families of random variables such that concepts are considerable us in deriving inequalities in probability and statistics. Lehman introdued various concepts of positive dependence for bivariate random variables. A much stronger notions of positive dependence were later considered by Esary, Proschan, and Walkup. Ahmed et al and Ebrahimi and Ghosh also obtained multivariate versions of various bivariate positive dependence as descrived by Lehman. See also Block al. Glaz and Johnson an Barlow and Proschan and the references there. Multivariate processes arise when instead of observing a single process we observe several processes, say $X_19t), \cdots, X_n(t)$ simultaneously. For example, in an engineering context we may want to study the simultaneous variation of current and voltage, or temperature, pressure and volume over time. In economics we may be interested in studying inflation rates and money supply, unemployment and interest rates. We could of course, study each quantity on its own and treat each as a separate univariate process. Although this would give us some information about each quantity it could never give information about the interrelationship between various quantities. This leads us to introduce some concepts of positive and for multivariate stochastic processes. The concepts of positive dependence have subsequently been extended to stochastic processes in different directions by many authors.

• Korean Chemical Engineering Research
• /
• 제45권1호
• /
• pp.87-92
• /
• 2007

최근 공정의 이상을 감지하고 진단하기 위한 공정 모니터링 시스템의 개발이 공정 시스템 분야에서 많은 주목을 받고 있다. 공정으로부터 얻어지는 데이터는 공정의 특성에 대한 유용한 정보를 제공하고 이는 공정의 모델링과 모니터링 그리고 제어에 사용된다. 현대의 화학 및 환경 공정은 고차원적인 특성과 변수간의 강한 상관관계와 동특성 그리고 비선형적 특성을 가지고 있어 모델 기반 접근을 통해 공정을 분석하는 것을 쉽지 않다. 이러한 모델 기반 접근의 한계를 극복하기 위해 많은 시스템 엔지니어와 연구자들이 주성분 분석법(principal component analysis, PCA) 또는 부분 최소 자승법(partial least squares, PLS)과 같은 다변량 분석을 접목한 통계 기반 접근법에 초점을 맞추고 있다. 또한 동특성, 비선형성 등과 같은 특성을 가진 공정에 적용하기 위해 많은 다변량 분석법들이 보완되었다. 여기에서는 동적 주성분 분석법(dynamic PCA)과 케노니컬 변수 분석법(canonical variate analysis)을 이용한 결측 데이터의 예측법과 공정 변수의 복원을 통한 센서 오작동의 판별법에 대해 언급해 보고자 한다.

• Journal of the Korean Data and Information Science Society
• /
• 제23권5호
• /
• pp.1037-1044
• /
• 2012

Multivariate control charts for effectively monitoring every component in the dispersion matrix of multivariate normal process are considered. Through the numerical results, we noticed that the multivariate control charts based on sample statistic $V_i$ by Hotelling or $W_i$ by Alt do not work effectively when the correlation coefficient components in dispersion matrix are increased. We propose a combined procedure monitoring every component of dispersion matrix, which operates simultaneously both control charts, a chart controlling variance components and a chart controlling correlation coefficients. Our numerical results show that the proposed combined procedure is efficient for detecting changes in both variances and correlation coefficients of dispersion matrix.