• Journal of the Korean earth science society
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• v.26 no.5
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• pp.404-416
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• 2005

The seasonal and regional distribution of precipitation in Korea, terms of the amount of precipitation per day, number of days, and intensity was analyzed using precipitation data from 1971 to 2000. The significance level of the linear trend of these data was also investigated using the analysis of variance of each variable. The amount of precipitation per day less than 80 mm per day appeared in the Honam area which also shows a large number of precipitation day value during the fall and winter. However, the lowest amuont of precipitation per day was shown in the Youngnam area. The positive trend of the annual precipitation amount has also been detected in all stations except for a few station in Honam, and the positive trend of precipitation intensity is statistically significant in most of the stations at the Chungcheong and Gyeonggi area. The linear trend of precipitation intensity in these area is found to be significant at the $5\%$ level.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.865-877
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• 2009

While characterized initially as an urban-scale pollutant, ozone has increasingly been recognized as a regional and even global-scale phenomenon. The complexity of environmental data dynamics often requires models covering non-linearity. This study deals with modeling ozone with meteorology in Seoul area. The relationships are used to construct a nonlinear regression model relating ozone to meteorology. The model can be used to estimate that part of the trend in ozone levels that cannot be accounted for by trends in meteorology.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.353-371
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• 2022

This paper analyzes death counts after World War II of several countries to identify and to compare their stochastic structures. The stochastic structures that this paper entertains are three structural time series models, a local level with a random walk model, a fixed local linear trend model and a local linear trend model. The structural time series models assume that a time series can be formulated directly with the unobserved components such as trend, slope, seasonal, cycle and daily effect. Random effect of each unobserved component is characterized by its own stochastic structure and a distribution of its irregular component. The structural time series models use the Kalman filter to estimate unknown parameters of a stochastic model, to predict future data, and to do filtering data. This paper identifies the best-fitted stochastic model for three types of death counts (Female, Male and Total) of each country. Two diagnostic procedures are used to check the validity of fitted models. Three criteria, AIC, BIC and SSPE are used to select the best-fitted valid stochastic model for each type of death counts of each country.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.945-957
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• 2009

Repeated measurement data among several treatments with a control is often used in the field of medicine study. In this paper, we suggest a method for comparison of the linear trend of responds followed time among several treatments with a control based on repeated measurement data. First, we estimate slope from each subject and generate samples using the slope estimated previous. And then, we test the difference among treatment with a control by ANOVA F test, Jonckheere-Terpstra test, updated control group procedure using generated samples. Monte Carlo Simulation is adapted to compare the power and experimental significance levels in various configuration.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.105-117
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• 2004

In this paper, a new method is developed for estimating the mean of a population which has a linear trend. This method involves drawing a sample by the modified systematic sampling, and then estimating the population mean with an adjusted estimator, not with the sample mean itself. We use the method of least squares in determining the adjusted estimator. The proposed method is shown to be more and more efficient as the linear trend becomes stronger. It turns out to be relatively efficient as compared with the conventional methods if $\sigma$$^2$the variance of the random error term in the infinite superpopulation model, is not very large.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.37-46
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• 2023

The main aim of the article is to solve the problem of automating price monitoring using marketing forecasting methods and Excel functionality under martial law. The study used the method of algorithms, trend analysis, correlation and regression analysis, ANOVA, extrapolation, index method, etc. The importance of monitoring consumer price developments in market pricing at the macro and micro levels is proved. The introduction of a Dummy variable to account for the influence of martial law in market pricing is proposed, both in linear multiple regression modelling and in forecasting the components of the Consumer Price Index. Experimentally, the high reliability of forecasting based on a five-factor linear regression model with a Dummy variable was proved in comparison with a linear trend equation and a four-factor linear regression model. Pessimistic, realistic and optimistic scenarios were developed for forecasting the Consumer Price Index for the situation of the end of the Russian-Ukrainian war until the end of 2023 and separately until the end of 2024.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.345-349
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• 2007

In this study a fuzzy trend monitoring method for detecting the engine mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration. etc. Using engine condition data set as a input which generated by linear regression analysis of real engine instrument data, an application of fuzzy logic in diagnostics estimate a cause of fault in each components.

• Atmosphere
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• v.24 no.3
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• pp.379-390
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• 2014

This study explores the long-term trends of surface air temperatures in 11 KMA stations over the period of 1960~2012. Both linear and nonlinear trends are examined for the $95^{th}$, $50^{th}$, and $5^{th}$ percentiles of daily maximum ($T_{max}$) and minimum temperatures ($T_{min}$) by using quantile regression method. It is found that in most stations linear trends of $T_{max}$ and $T_{min}$ are generally stronger in winter than in summer, and warming trend of the $5^{th}$ percentile temperature (cold extreme) is stronger than that of the $95^{th}$ percentile temperature (warm extreme) in both seasons. The nonlinear trends, which are evaluated by the second order polynomial fitting, show a strong nonlinearity in winter. Specifically, winter temperatures have increased until 2000s but slightly decreased afterward in all percentiles. This contrasts with the $95^{th}$ and $50^{th}$ percentiles of summer $T_{min}$ that show a decreasing trend until 1980s then an increasing trend. While this result is consistent with a seasonal dependence of the recent global warming hiatus, most of the nonlinear trends are statistically insignificant, making a quantitative attribution of nonlinear temperature trends challenging.

• The Korean Journal of Pain
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• v.34 no.3
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• pp.322-331
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• 2021

Background: The present study employed National Health Insurance Data to explore complex regional pain syndrome (CRPS) updated epidemiology in a Korean context. Methods: A CRPS cohort for the period 2009-2016 was created based on Korean Standard Classification of Diseases codes alongside the national registry. The general CRPS incidence rate and the yearly incidence rate trend for every CRPS type were respectively the primary and secondary outcomes. Among the analyzed risk factors were age, sex, region, and hospital level for the yearly trend of the incidence rate for every CRPS. Statistical analysis was performed via the chi-square test and the linear and logistic linear regression tests. Results: Over the research period, the number of registered patients was 122,210. The general CRPS incidence rate was 15.83 per 100,000, with 19.5 for type 1 and 12.1 for type 2. The condition exhibited a declining trend according to its overall occurrence, particularly in the case of type 2 (P < 0.001). On the other hand, registration was more pervasive among type 1 compared to type 2 patients (61.7% vs. 38.3%), while both types affected female individuals to a greater extent. Regarding age, individuals older than 60 years of age were associated with the highest prevalence in both types, regardless of sex (P < 0.001). Conclusions: CRPS displayed an overall incidence of 15.83 per 100,000 in Korea and a declining trend for every age group which showed a negative association with the aging shift phenomenon.