• The Korean Journal of Applied Statistics
• /
• v.34 no.4
• /
• pp.589-597
• /
• 2021

In simple and multiple regression, there is a difference in the meaning of regression coefficients, and not only are the estimates of regression coefficients different, but they also have different signs. Understanding the relative contribution of explanatory variables in a regression model is an important part of regression analysis. In a standardized regression model, the regression coefficient can be interpreted as the change in the response variable with respect to the standard deviation when the explanatory variable increases by the standard deviation in a situation where the values of the explanatory variables other than the corresponding explanatory variable are fixed. However, the size of the standardized regression coefficient is not a proper measure of the relative importance of each explanatory variable. In this paper, the estimator of the regression coefficient in multiple regression is expressed as a function of the correlation coefficient and the coefficient of determination. Furthermore, it is considered in terms of the effect of an additional explanatory variable and additional increase in the coefficient of determination. We also explore the relationship between estimates of regression coefficients and correlation coefficients in various plots. These results are specifically applied when there are two explanatory variables.

• The Korean Journal of Applied Statistics
• /
• v.34 no.1
• /
• pp.1-8
• /
• 2021

Abstract concepts in social research must use measurement tools that are assured of validity and reliability. Observation score derived by a measurement tool can be divided into a valid observation score, a biased observation score, and an error. The presence or absence of a biased value is associated with validity, and the presence or absence of an error value is associated with reliability. There are many techniques for seeing whether a measurement tool is valid and reliable. For example, there are construct validity using factor analysis and internal consistency based on the Cronbach alpha. In this study, the calculation of the Cronbach alpha is derived through a sample, so we suggest an estimator of the Cronbach alpha under complex sample design and nonresponse. In a simulation, the proposed method is compared with many other existing estimators of Cronbach alpha under a multivariate normal distribution.

• Journal of Korea Trade
• /
• v.24 no.8
• /
• pp.121-132
• /
• 2020

Purpose - The principle aim of this study is to further investigate the relationship between market diversity and export performance. We examine the benefits and costs of geographic market diversity regarding the number of countries exported to by firms on their export performance. Based on the financial risk reduction model and the entry costs model, we propose a way to incorporate the costs and benefits aspects of market diversity. Design/methodology - To empirically investigate our research question, the curvilinear relationship between market diversity and export performance, we built a secondary panel data set between 2015 and 2019, containing 17,863 observations of Korean exporting companies. A generalized least squares panel estimator with fixed effects was employed to test the hypothesis, and the statistical package, Stata 14, was used. Findings - Our main findings are as follows: As market diversity increases, export performance increases because exporters can diversify and reduce financial risks in export markets. However, the relationship between the two does not grow. As it peaks, the entry costs increase due to the high market diversity, thereby outweighing the benefits, leading, eventually to decrease in the export performance. Consequently, there is an inverted U-shaped relationship between market diversity and export performance. Originality/value - In the export and trade literature, the impact of market diversity on export performance has not been addressed yet, despite the importance of this subject. Many scholars have assumed a positive linear relationship between the two, considering only the decrease in market risks as the number of overseas markets increases, without examining the increase in the entry and management costs. Therefore, our study contributes by providing a new perspective for analyzing the characteristics and outcomes of market diversity.

• The Korean Journal of Applied Statistics
• /
• v.36 no.4
• /
• pp.309-322
• /
• 2023

The Hawkes process is a point process with self-exciting characteristics. It has been mainly used to describe seismic phenomena in which aftershocks occur due to the main earthquake. Recently, it has been used to explain various phenomena with self-exciting properties, such as the spread of infectious diseases and the spread of news on SNS. The Hawkes process can be flexibly modified according to the characteristics of events by using various types of excitation functions. Since it is difficult to implement a maximum likelihood estimator numerically, estimation methods have been improved until recently. In this paper, the conditional intensity function and excitation function are explained to describe the Hawkes process. Then, existing examples of Hawkes processes used in seismic, epidemiological, criminal, and financial fields are described and estimation methods are introduced. I analyze earthquakes that occurred in gyeongsang-do, Korea from November 2017 to December 2022, using R package ETAS.

• Korean Journal of Radiology
• /
• v.21 no.11
• /
• pp.1239-1247
• /
• 2020

Objective: To report the mid-term results of a single-center randomized controlled trial comparing drug-coated balloon angioplasty (DBA) and plain balloon angioplasty (PBA) for the treatment of dysfunctional radiocephalic arteriovenous fistulas (RCAVFs). Materials and Methods: In this prospective study, 39 patients (mean age, 62.2 years; 21 males, 18 females) with RCAVFs failing due to juxta-anastomotic stenosis were randomly assigned to undergo either both DBA and PBA (n = 20, DBA group) or PBA alone (n = 19, PBA group) between June 2016 and June 2018. Primary endpoints were technical and clinical success and target lesion primary patency (TLPP); secondary outcomes were target lesion secondary patency (TLSP) and complication rates. Statistical analysis was performed using the Kaplan-Meier product limit estimator. Results: Demographic data and baseline clinical characteristics were comparable between the groups. Technical and clinical success rates were 100% in both groups. There was no significant difference between the groups in the mean duration of TLPP (DBA group: 26.7 ± 3.6 months; PBA group: 27.0 ± 3.8 months; p = 0.902) and TLSP (DBA group: 37.3 ± 2.6 months; PBA group: 40.4 ± 1.5 months; p = 0.585). No procedural or post-procedural complications were identified. Conclusion: Paclitaxel-coated balloon use did not significantly improve TLPP or TLSP in the treatment of juxta-anastomotic stenosis of dysfunctional RCAVFs.

• The Korean Journal of Applied Statistics
• /
• v.37 no.3
• /
• pp.283-295
• /
• 2024

Many studies have been conducted to properly handle nonresponse. Recently, many nonresponse imputation methods have been developed and practically used. Most imputation methods assume MCAR (missing completely at random) or MAR (missing at random). On the contrary, there are relatively few studies on imputation under the assumption of MNAR (missing not at random) or NN (nonignorable nonresponse) that are affected by the study variable. The MNAR causes Bias and reduces the accuracy of imputation whenever response probability is not properly estimated. Lee and Shin (2022) proposed a nonresponse imputation method that can be applied to nonignorable nonresponse assuming homoscedasticity in super-population model. In this paper we propose an generalized version of the imputation method proposed by Lee and Shin (2022) to improve the accuracy of estimation by removing the Bias caused by MNAR under heteroscedasticity. In addition, the superiority of the proposed method is confirmed through simulation studies.

• Survey Research
• /
• v.10 no.3
• /
• pp.157-172
• /
• 2009

For a survey research in which the characteristics of the population of interest are investigated from a sample, representativeness of the sampling frame is one of the most important part to be considered. If the sampling frame fails to represent the population properly, statistical procedures based on the even efficient sampling design result in significant nonsampling biases and thus the statistical validities of the results could be damaged. But the construction of the reliable sampling frame that covers the population properly costs money and time and thus the sampling frame based on a census or a large scale survey is often used in practice. For example, the sampling frame based on the population households census is used for many household surveys in Korea. But due to the time difference between the census and a survey of interest, the sampling frame constructed from the census is expected to fail to cover the population of interest. Especially, one could expect a large amount of population and household movement in a large city like Seoul. Thus in our research, we considered the construction of new sampling frame and the procedure of sample selection for 2008 Seoul survey. We analyzed the sampling frame based on 2005 population households census and found that it does not represent the population properly. Thus, we proposed a new sampling frame based on resident registration DB for 2008 Seoul survey. We also proposed the sampling weights and estimator of the population mean based on the sample selected from the newly constructed sampling frame.

• Communications for Statistical Applications and Methods
• /
• v.17 no.2
• /
• pp.293-308
• /
• 2010

In this paper, we examine the forecasting KOSPI 200 realized volatility by volatility measurements. The empirical investigation for KOSPI 200 daily returns is done during the period from 3 January 2003 to 29 June 2007. Since Korea Exchange(KRX) will launch VKOSPI futures contract in 2010, forecasting VKOSPI can be an important issue. So we analyze which volatility measurements forecast VKOSPI better. To test this hypothesis, we use 5-minute interval returns to measure realized volatilities. Also, we propose a new methodology that reflects the synchronized bidding and simultaneously takes it account the difference between overnight volatility and intra-daily volatility. The t-test and F-test show that our new realized volatility is not only different from the realized volatility by a conventional method at less than 0.01% significance level, also more stable in summary statistics. We use the correlation analysis, regression analysis, cross validation test to investigate the forecast performance. The empirical result shows that the realized volatility we propose is better than other volatilities, including historical volatility, implied volatility, and convention realized volatility, for forecasting VKOSPI. Also, the regression analysis on the predictive abilities for realized volatility, which is measured by our new methodology and conventional one, shows that VKOSPI is an efficient estimator compared to historical volatility and CRR implied volatility.

• The Korean Journal of Applied Statistics
• /
• v.29 no.7
• /
• pp.1459-1473
• /
• 2016

Both longitudinal data and survival data are collected simultaneously in longitudinal data which are observed throughout the passage of time. In this case, the effect of the independent variable becomes biased (provided that sole use of longitudinal data analysis does not consider the relation between both data used) if the missing that occurred in the longitudinal data is non-ignorable because it is caused by a correlation with the survival data. A joint model of longitudinal data and survival data was studied as a solution for such problem in order to obtain an unbiased result by considering the survival model for the cause of missing. In this paper, a joint model of the longitudinal zero-inflated count data and survival data is studied by replacing the longitudinal part with zero-inflated count data. A hurdle model and proportional hazards model were used for each longitudinal zero inflated count data and survival data; in addition, both sub-models were linked based on the assumption that the random effect of sub-models follow the multivariate normal distribution. We used the EM algorithm for the maximum likelihood estimator of parameters and estimated standard errors of parameters were calculated using the profile likelihood method. In simulation, we observed a better performance of the joint model in bias and coverage probability compared to the separate model.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.