• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.55-72
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• 1996

It is essential in modern industry that quality and procuctivity are improved continuously. To accomplish this purpose, quality control must be maintained in all parts of a company. Recently, some tire manufacture companies are beginning to show interest in quality control. They have tried to achive some results through the statistical analysis for the experimental data which has accumulated up to now and then they strive to determine the structural relationship between the design factors in tire construction and tire performance characteristics. The measurement data obtained from the construction engineering is given in multivariate form owing to the various properties found in tire design components as wll as in performance. Also it may be existed the relationship among the multimple response variables. Thus we proposes the use of the biplot graphical display as an analytic tool of data matrices with complex respects. The proposed biplots are also availalbe to understand both the underlying structure of the data and the roles played by the different components. In particular, we consider the matter of how best to use the biplots in the maltivariate analysis of variance and multiple response data. Finally we apply this method to analyze the actual data.

• Water for future
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• v.24 no.4
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• pp.67-72
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• 1991

Various modeling approaches to study along term behavior of streamflow or groundwater storagge have been conducted. In this study, a Multivariate AR (1) Model has been applied to generate monthly flows of the one key station which has historical flows using monthly flows of the three subordinate stations. The Model performance was examined using statistical comparisons between the historical and generated monthly series such as mean, various, skewness. Also, the correlation coefficients(lag-zero, and lag-one)between the two monthly flows were compared. The results showed that the modeled generated flows were statistically similar to the historical flows.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.981-996
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• 2008

Statistical matching techniques whose aim is to achieve a complete data file from different sources. Since the statistical matching method proposed by Rubin (1986) assumes the multivariate normality for data, using this method to data which violates the assumption would involve some problems. This research proposed the statistical matching method using robust regression as an alternative to the linear regression. Furthermore, we carried out a simulation study to compare the performance of the robust regression model and the linear regression model for the statistical matching.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.113-122
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• 2016

Longitudinal data often occur in prospective follow-up studies. Joint model for longitudinal data and failure time has been applied on several works. In this paper, we extend it to the case where longitudinal data involve informative observation time process as well as competing risks survival times. We use a likelihood approach and derive an EM algorithm to obtain maximum likelihood estimate of parameters. A suggested joint model allows us to make inferences for three components: longitudinal outcome, observation time process and competing risk failure time. In addition, we can test the association among these components. In this paper, liver cirrhosis patients' data is analyzed. The relationship between prothrombin times measured at irregular visiting times and drop outs is investigated with a joint model.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.271-280
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• 2017

Real-estate values and related economics are often the first read newspaper category. We are concerned about the opinions of experts on the forecast for real estate prices. The Box-Jenkins ARIMA model is a commonly used statistical method to predict housing prices. In this article, we tried to predict housing prices by combining independent component analysis (ICA) in multivariate data analysis and the Box-Jenkins ARIMA model. The two independent components for both the selling price index and the long-term rental price index were extracted and used to predict the future values of both indices. In conclusion, it has been shown that the actual indices and the forecast indices using ICA are more comparable to the forecasts of the ARIMA model alone.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.1068-1071
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• 2005

최근 들어 기술을 담보로 하는 신용금융의 역할이 증대되면서 자금지원 대상기업의 기술평가 시스템 구축이 중요한 과제가 되고 있다. 국내에서는 기업 보유의 기술경영성과를 측정하여 한정된 자원의 효율적 배분을 위한 민간 투, 융자를 위한 기술신용평가모형'이 제시되었다 본 연구에서는 기술신용평가모델의 평가항목 타당성을 실증 분석한다. 모형의 항목 분류가 적절하게 되었는지를 검증하기 위하여 구조적 타당성을 평가하며 통계적 유의성을 검증하여 신뢰성을 평가한다. 구조적 타당성 검정을 위해 확인 요인분석을 수행하며 평가모형의 신뢰성을 검증하기 위해서는 다변량 통계방법 중의 하나인 판별분석을 수행한다. 본 연구는 기술개발 성공 및 부실발생의 예측력을 갖는 기술신용평가 시스템 구축을 위한 기초 자료로 활용될 수 있을 것이다.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.291-299
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• 2008

This paper discusses model building for repeated binary response data with different time-dependent covariates each occasion. Since repeated measurements data are having correlated structure, weighed least squares(WLS) methodology is applied. Repeated measures designs are usually having different sizes of experimental units like split-plot designs. However repeated measures designs differ from split-plot designs in that the levels of one or more factors cannot be randomly assigned to one or more of the sizes of experimental units in the experiment. In this case, the levels of time cannot be assigned at random to the time intervals. Because of this nonrandom assignment, the errors corresponding to the respective experimental units may have a covariance matrix. So, the estimates of effects included in a suggested logit model are obtained by using covariance structures.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.297-310
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• 2002

Liang and Zeger proposed generalized estimating equations(GEE) for analyzing repeated data which is discrete or continuous. GEE model can be extended to model for repeated categorical data and its estimator has asymptotic multivariate normal distribution in large sample sizes. But GEE is based on large sample asymptotic theory. In this paper, we study the properties of GEE estimators for repeated ordinal data in small sample sizes. We generate ordinal repeated measurements for two groups using two methods. Through Monte Carlo simulation studies we investigate the empirical type 1 error rates, powers, relative efficiencies of the GEE estimators, the effect of unequal sample size of two groups, and the performance of variance estimators for polytomous ordinal response variables, especially in small sample sizes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.887-896
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• 1994

It is common practice in the case of optimum reservoir operation model that the reservoir inflow series are generated by stochastic model with keeping other variable such as water demands from the reservoir constant. However, when the input and output of the water distribution system have close relationship the output variables can be stochastically generated in relation with the input variables. In the present study the reservoir inflow series, the input of the system, is generated by periodic autoregressive model with constant parameter, and the agricultural water demand series, the output, is generated using periodic multivariate autoregressive model with constant parameter. The time period of the data series generated is taken as 10-day which is the common period used for agricultural water uses. The results of data generation by two different models showed that the periodic stochastic models well represent the characteristics of the historical time series, and that in the case of generating model for agricultural demand series it has closer relation with reservoir inflow than with the series itself.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.825-835
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• 2008

Multivariate GARCH(MGARCH) has been useful in financial studies and econometrics for modeling volatilities and correlations between components of multivariate time series. An obvious drawback lies in that the number of parameters increases rapidly with the number of variables involved. This thesis tries to resolve the problem by using dimension reduction technique. We briefly review both factor models for dimension reduction and the MGARCH models including EWMA (Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model). We create meaningful portfolios obtained after reducing dimension through statistical factor models and fundamental factor models and in turn these portfolios are applied to MGARCH. In addition, we compare portfolios by assessing MSE, MAD(Mean absolute deviation) and VaR(Value at Risk). Various financial time series are analyzed for illustration.