• Journal of the Korean Statistical Society
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• v.34 no.3
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• pp.173-183
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• 2005

The Box-Cox power transformation is generally used for variance stabilization. Recently, Shin and Kang (2001) showed, under the Box-Cox transformation, invariant properties to the original model under the large mean and relatively small variance assumptions in time series analysis. In this paper we obtain some invariant properties in spatial statistics. Spatial statistics, Invariant Property, Variogram, Box-Cox power Transformation.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.7 no.1
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• pp.91-99
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• 1999

On the basis of KTMPO visibility data measured for 17 years(Jan.1983 to Oct.1999), time-series data analysis is accomplished for regression trend, cyclical periodicity, dependency of these data in this paper. After that, to predict visibility in KIMPO international airport a probability model is presented using this statistic probability characteristics.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.66-76
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• 2019

Recently, natural disasters such as floods and droughts are frequently occurred due to climate change and the damage is also increasing. Wetland is known to play an important role in reducing and minimizing the damage. In particular, water level variability needs to be analyzed in order to understand the various functions of wetland as well as the reduction of damage caused by natural disaster. Therefore, in this study, we fitted water level series of Upo wetland in Changnyeong, Gyeongnam province to a proper time series model and residual test was performed to confirm the appropriateness of the model. In other words, ARIMA model was constructed and its residual tests were performed using existing nonparametric statistics, BDS statistic, and Close Returns Histogram(CRH). The results of residual tests were compared and especially, we showed the applicability of CRH to analyze the residuals of time series model. As a result, CRH produced not only accurate randomness test result, but also produced result in a simple calculation process compared to the other methods. Therefore, we have shown that CRH and BDS statistic can be effective tools for analyzing residual in time series model.

• The Research Journal of the Costume Culture
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• v.11 no.2
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• pp.253-262
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• 2003

The purposes of the study were (1) to analysis Korean traditional wedding costumes through families's wedding photographs from 1940 to 2000, and (2) to categorize bride and groom's costumes according to the wedding time by means of a time series analysis. (3) to find out functional relationship among changes in garment types, garment details, embellishments and colors. The study was a documentary research and data were collected from 390 family wedding photographs by a convenient sampling. The data were analyzed by qualitative and quantitative method and the statistic used were frequency, content analysis, and cross-tab analysis. The results were as follows; First, the garments of wedding couples were categorized into 5 period according to garment's characteristics. 1. The period between 1940～1959 : Korean traditional wedding costumes and western style wedding costumes were existed together in Korean wedding culture. 2. The period between 1960～early 1970's western wedding costumes were dominated. 3. The period of late 1970's : wedding couple's costumes became more formal and decorative. 4. The period of 1980's : introducing see-through materials for brides and tuxedo suit for grooms. 5. The period of 1990's : extravagance in shapes and exposure. Second, there were significant relationships among brides's dress types and neckline, glove length, embellishments and transparency of materials and among groom's garment types and necktie types, types and color of shirts, vests. Third, the time series analysis of bride and groom's outfit produced 5 schematic expressions of wedding outfits according to the period.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.92-100
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• 2006

In many common control charting situations, the statistic to be charted can be viewed as the output of a linear filter applied to the sequence of process measurement data. In recent work that has generalized this concept, the charted statistic is the output of a general linear filter in impulse response form, and the filter is designed by selecting its impulse response coefficients in order to optimize its average run length performance. In this work, we restrict attention to the class of all second-order linear filters applied to the residuals of a time series model of the process data. We present an algorithm for optimizing the design of the second-order filter that is more computationally efficient and robust than the algorithm for optimizing the general linear filter. We demonstrate that the optimal second-order filter performs almost as well as the optimal general linear filter in many situations. Both methods share a number of interesting characteristics and are tuned to detect any distinct features of the process mean shift, as it manifests itself in the residuals.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.463-470
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• 2016

In analysing a time series on the frequency domain, the spectral estimator (or periodogram) is a very useful statistic to identify the periods of a time series. However, the spectral estimator is very sensitive in nature to outliers, so that the spectral estimator in terms of M-estimation has been studied by some researchers. Pak (2001) proposed an empirical method to choose a tuning parameter for the Huber's M-estimating function. In this article, we try to implement Pak's estimation proposal in the spectral estimator. We use the Korean housing price index as an example data set for comparing various M-estimating results.

• Journal of Environmental Science International
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• v.16 no.3
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• pp.369-376
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• 2007

This study was conducted to evaluate water quality in coastal sea of Kunsan using multivariate analysis. The analysis data in Coastal Sea of Kunsan use of surveyed data by the NFRDI from April 2000 to November 2002. Twelve water Quality parameter were determined on each sample. The results was summarized as follow ; Water quality in coastal sea of Kunsan could be explained up to 62.782% by four factors which were included in loading of nitrogen-nutrients by Keum river(24.688%), suspended solids variation (12.180%), seasonal climate variation (18.367%) and variation of DIP (10.546%). To analyze spatially and monthly variation by factor score, it was divided by inner area and outer area spatially, and spring and summer monthly. The result of time series analysis by factor score, inner area of Kunsan coastal sea(St.1 and St. 2) was the most affected by nitrogen-nutrient and suspended solids due to runoff by Keum river. It could be suggested from these results that it is important to reduce tile pollution loads from Kuem river for the control of the water quality in coastal sea of Kunsan.

• Smart Structures and Systems
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• v.5 no.1
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.889-896
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• 2012

In this paper, we propose a statistic to measure investor sentiment. It is a usual phenomenon that an asymmetric volatility (referred to as the leverage effect) is observed in financial time series and is more sensitive to bad news rather than good news. In a bubble state, investors tend to continuously speculate on financial instruments because of optimism about the future; subsequently, prices tend to abnormally increase for a long time. Estimators of the transformation parameter and the skewness based on Yeo-Johnson transformed GARCH models are employed to check whether a bubble or abnormality exist. We verify the appropriacy of the proposed interpretation through analyses of KOSPI and NIKKEI.