• 응용통계연구
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• 제32권6호
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• pp.909-922
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• 2019

벌점화 추정 기법 중 adaptive LASSO 방법은 모형 선택과 모수 추정을 동시에 할 수 있는 유명한 방법으로 이미 정상 자기회귀모형에서 연구된 적이 있다. 본 논문에서는 이를 확장하여 확률보행과정과 같은 비정상 자기회귀모형에서 adaptive LASSO 추정량이 갖는 성질을 모의실험을 통해 연구하였다. 다만 비정상 자기회귀모형에서는 단위근의 존재 여부를 판단하는 것과 모형의 차수를 선택하는 것이 가장 중요하므로, 이를 위해 원 자기회귀모형이 아닌 ADF 검정에서 고려하는 회귀모형으로 변환하여 adaptive LASSO를 적용하였다. 일반적으로 Adaptive LASSO를 적용할 때 조절모수의 선택이 가장 중요한 문제이며, 본 논문에서는 교차검증, AIC, BIC 세 가지 방법을 이용하여 조절모수를 선택하였다. 모의실험 결과를 보면, 이 중에서 BIC가 최소가 되도록 선택한 조절모수에 대응되는 adaptive LASSO 추정량이 단위근의 존재 여부를 잘 판단할 뿐만 아니라 자기회귀모형의 차수 또한 비교적 정확하게 선택함을 확인할 수 있다.

• 자원ㆍ환경경제연구
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• 제15권3호
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• pp.405-423
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• 2006

본 연구는 자본과 노동의 요소투입물 증가가 환경오염의 증가를 유발하고 기술축적자체도 환경오염을 유발한다는 가설모형을 설정하고, 사회계획자모형과 환경오염방지활동이 이루어지지 않는 시장경제모형을 구축하여 이론모형을 도출한다. 도출된 이론모형을 이용하여 환경오염변수와 경제성장률(또는 국민소득 수준) 간에 선형이 아닌 비선형동학(nonlinear dynamics) 관계가 존재하는지를 분석하기 위하여 변수의 부드러운 곡면전환이 이루어지는 평활전이자기회귀모형(Smooth Transition Autoregressive : STAR)을 사용하였다. 서울시 산업생산지수와 대기오염도를 이용한 실증분석에서 경제성장률과 환경오염변수 간에 비선형 동태적인 관계와 비선형 그랜저 인과관계가 존재하는 것으로 나타나 서울지역에서의 환경쿠즈네츠곡선가설이 성립하고 있음을 간접적으로 확인하였다. 그러나 그 해석에는 한계가 있음을 지적하였다.

• Smart Structures and Systems
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• 제15권5호
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• pp.1329-1344
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• 2015

The stiffness of a structure is one of several structural signals that are useful indicators of the amount of damage that has been done to the structure. To accurately estimate the stiffness, an equation of motion containing a stiffness parameter must first be established by expansion as a linear series model, a Taylor series model, or a power series model. The model is then used in multivariate autoregressive modeling to estimate the structural stiffness and compare it to the theoretical value. Stiffness assessment for modeling purposes typically involves the use of one of three statistical model refinement approaches, one of which is the efficient Akaike information criterion (AIC) proposed in this paper. If a newly added component of the model results in a decrease in the AIC value, compared to the value obtained with the previously added component(s), it is statistically justifiable to retain this new component; otherwise, it should be removed. This model refinement process is repeated until all of the components of the model are shown to be statistically justifiable. In this study, this model refinement approach was compared with the two other commonly used refinement approaches: principal component analysis (PCA) and principal component regression (PCR) combined with the AIC. The results indicate that the proposed AIC approach produces more accurate structural stiffness estimates than the other two approaches.

• 산업경영시스템학회지
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• 제35권3호
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• pp.52-61
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• 2012

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is intractable in mathematical derivations, and thus any modification on the statistic can not be favorably made. We propose residual-based robust CUSUM control charts for monitoring autocorrelated processes. In order to incorporate the effects of ARMA modeling errors into the design method, we modify parameters (reference value and decision interval) of CUSUM control charts using the approximate expected variance of residuals generated in model uncertainty, rather than directly modify the form of the CUSUM statistic. The expected variance of residuals is derived using a second-order Taylor approximation and the general form is represented using the order of ARMA models with the sample size for ARMA modeling. Based on the Monte carlo simulation, we demonstrate that the proposed method can be effectively used for statistical process control (SPC) charts, which are robust to ARMA modeling errors.

• Journal of the Korean Statistical Society
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• 제13권2호
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• pp.87-106
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• 1984

It ${X_t}$ is a sequence of independent identically distributed normal random variables, then the conditional probability density of $X_1, X_2, \cdots, X_n$ given the first p+1 sample autocovariances converges to the maximum entropy probability density satisfying the corresponding covariance constraints as the length of the sample sequence tends to infinity. This establishes that the maximum entropy probability density and the associated Gaussian autoregressive process arise naturally as the answers of conditional limit problems.

• Journal of the Korean Statistical Society
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• 제17권2호
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• pp.101-120
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• 1988

A time series model with Laplacian (double-exponential) marginal distribution, NLAR(2), was proposed by Dewald and Lewis (1985). The special cases of NLAR(2) process and their properties are considered. Extensions to the NLAR(p) is discussed. It is shown that the NLAR(1) satisfies the strong-mixing conditions, hence the model-free prediction interval using the sample quantiles can be obtained.

• Journal of the Korean Data and Information Science Society
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• 제6권2호
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• pp.41-50
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• 1995

In this paper, an iterative procedure, which detects the location of the outliers and the joint estimates of the outliers effects and the model parameters in the autoregressive moving average model with two types of outliers, is proposed. The performance of the procedure is compared with the one in Chen and Liu(1993) through the Monte Carlo simulation. The proposed procedure is very robust in the sense that applies the procedures to the stationary time series model with any types of outliers.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.157.4-157
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• 2001

An effective approach to detect and diagnose multiple failures in a dynamic system is proposed for the case where the measurement noise is correlated sequentially in time. It is based on the modified interacting multiple-model (MIMM) estimation algorithm in which a generalized decorrelation process is developed by employing the autoregressive (AR) model for the correlated measurement noise. Numerical example for the nuclear steam generator is provided to illustrate the enhanced performance of the proposed algorithm.

• 산업경영시스템학회지
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• 제15권26호
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• pp.181-187
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• 1992

본 논문은 CIMS에서 적응되는 ARMA 공정제어의 새로운 3단계절차를 제안한다. 첫번째 단계는 다변량 ARMA모델을 식별하여 모수를 추정하고, white noise로 진단된 잔차 series에 대하여 다변량 제어통계량(즉, 다변량 Hotelling T$^2$통계량, 다변량 CUSUM, 다변량 EWHA 통계량, 다변량 MA 통계량)등을 계산한다. 마지막으로 본 논문에서 제안한 8가지 다변량 제어통계량을 상호비교하여 이상점을 발견한다.

• Journal of the Korean Statistical Society
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• 제36권1호
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• pp.149-156
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• 2007

This study suggests a new method for testing seasonal unit roots in a momentum threshold autoregressive (MTAR) process. This sign test is robust against heteroscedastic or heavy tailed errors and is invariant to monotone data transformation. The proposed test is a seasonal extension of the sign test of Park and Shin (2006). In the case of partial seasonal unit root in an MTAR model, a Monte-Carlo study shows that the proposed test has better power than the seasonal sign test developed for AR model.