• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.169-183
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• 2009

The simplicity and wide application of Greenberg et al. (1971) prompts to propose a set of alternative estimators of population total for multi-character surveys that elicit simultaneous information on many. sensitive study variables. The proposed estimators take into account the already known rough value of the correlation coefficient between Y(the characteristic under study) and p(the measure of size). These estimators are biased, but it is expected that the extent of bias will be smaller, since the proposed estimators are suitable for situations in between those optimum for the usual estimators and the estimators based on multi-characters for no correlation. The relative efficiency of the proposed estimators has been studied under a super population model through empirical study. It has been found through simulation study that a choice of an unrelated variable in the Greenberg et al. (1971) model could be made based on its correlation with the auxiliary variable used at estimation stage in multi-character surveys.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.3
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• pp.458-467
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• 1994

It has been well known that the assumption of full state availability is one of the most important restrictions to the practical realization of VSCS. And several attempts to alleviate the assumption had been made. however, it is not easy to find a positive scheme among them. Recently, an output feedback variable structure control system(OFVSCS) was proposed and the effectiveness of the scheme was validated for the disturbance free systems. The purpose of this study is to propose a robust OFVSCS that have the robust properties against process parameter variations and external disturbances by extending the basic OFVSCS and to evaluate its control performances. The ROFVSES is composed of dynamic switching function and output feedback switching control inputs that are constructed by the use of the unknown vector modeling technique. With the proposed schems, existence of sliding mode is guaranteed and any nonzero bias can be suppressed in the face of disturbances and process parameter variations as far as well-known matching condition is satisfied. Due to the fact that the ROFVSCS is driven by small number of measured informations, the practical application of VSCS for the systems with unmeasurable states and for high order systems, the conventional schemes cannot be applied, is possible with the proposed scheme. It is noticeable that the implementation cast of VSCS can be considerably reduced without sacrifice of control performances by adopting ROFVSCS since there is no need to measure the states with high measurement cost.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.53-59
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• 2021

This research aims to examine the model of investor herding behavior in making investment decisions in the Indonesian capital market, which is influenced by social and information impacting on the value of the Book Value Per Share (BVPS). The latest stock market conditions show that most investors make the same error pattern in making investment decisions that result in losses. The experiment involves two independent variables, namely, information about BVPS and social influence. This study used a 2×2 factorial design laboratory experimental method. Data collection was carried out through treatment of a sample of 100 individual investors listed on the Indonesia Stock Exchange. Univariate Two-Way Analysis of Variance (ANOVA) statistical tool was used to test the independent variable on the dependent variable. Research results showed that the social influence originating from expert investors is more influential than the Book Value Per Share (BVPS) information on the behavior of herding investors in making investment decisions. These findings suggest that investors know their psychological factors, thereby increasing self-control and investment analysis skills. Further research can use psychological bias and other indicators of accounting relevant information such as Earning Per Share (EPS) to test herding behavior in investment decision making in the capital market.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.155-163
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• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

• Korea Journal of Hospital Management
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• v.18 no.3
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• pp.1-26
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• 2013

To improve the competitiveness of the hospital provides high quality medical services in a hospital coordinator role is emphasized. This study on customer orientation of the role ambiguity in order to identify the impact of degree of customer orientation were analyzed for demographic differences. Dependent variable, customer orientation affects role ambiguity as independent variables, and regression analysis were set. And the control variables are set to support situational factors, customer orientation on the role ambiguity and hierarchical regression analysis was performed. Obtained through empirical results are as follows: First, according to the demographic characteristics of the hospital coordinator customer orientation, the difference between gender and medical subjects are not shown. Age, education, work experience, job title, and the hospital on the pattern of customer orientation has shown a difference. Second, according to the hospital coordinator role ambiguity about its impact on customer orientation analysis can be a role implementation, job implementation, opinion communication in achieving customer orientation was negatively affected. Third, role ambiguity, and customer orientation factors for the moderating effects of organizational support for the role of customer orientation can role implementation, job implementation, opinion communication was a statistically significant. Fourth, the role ambiguity factors and customer orientation for the administrative support for the moderating effect of customer orientation and role implementation is significant, but job implementation, opinion communication were statistically significant. Fifth, the role ambiguity factors and customer support for customer orientation and customer orientation for the moderating effects of role performance and the opinion communication was not statistically significant. However, job implementation was statistically significant. The limitations of this study are as follows: First, role ambiguity, situational factors and support due to limitations of the variable factors that may affect the customer orientation of a number of factors were excluded. So many exogenous variables in the measurement process can affect. Second, the variables measured as problems of self-assessment by the variable measuring the respondent's bias may occur. Third, This study is difficult to generalize. In other words, several areas of the province conducted by the empirical results of the survey as a limit on the overall generalization can follow.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.533-545
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• 2017

Quantile regression models provide a variety of useful statistical information by estimating the conditional quantile function of the response variable. However, the traditional linear quantile regression model can lead to the distorted and incorrect results when analysing real data having a nonlinear relationship between the explanatory variables and the response variables. Furthermore, as the complexity of the data increases, it is required to analyse multiple response variables simultaneously with more sophisticated interpretations. For such reasons, we propose a multivariate quantile regression tree model. In this paper, a new split variable selection algorithm is suggested for a multivariate regression tree model. This algorithm can select the split variable more accurately than the previous method without significant selection bias. We investigate the performance of our proposed method with both simulation and real data studies.

• The Journal of Industrial Distribution & Business
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• v.8 no.5
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• pp.87-93
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• 2017

Purpose - This paper investigates whether managerial overconfidence is associated with firm-specific crash risk. Overconfidence leads managers to overestimate the returns of their investment projects, and misperceive negative net present value projects as value creating. They even use voluntary disclosures to convey their optimistic beliefs about the firms' long-term prospects to the stock market. Thus, the overconfidence bias can lead to managerial bad news hoarding behavior. When bad news accumulates and crosses some tipping point, it will come out all at once, resulting in a stock price crash. Research design, data and methodology - 7,385 firm-years used for the main analysis are from the KIS Value database between 2006 and 2013. This database covers KOSPI-listed and KOSDAQ-listed firms in Korea. The proxy for overconfidence is based on excess investment in assets. A residual from the regression of total asset growth on sales growth run by industry-year is used as an independent variable. If a firm has at least one crash week during a year, it is referred to as a high crash risk firm. The dependant variable is a dummy variable that equals 1 if a firm is a high crash risk firm, and zero otherwise. After explaining the relationship between managerial overconfidence and crash risk, the total sample was divided into two sub-samples; chaebol firms and non-chaebol firms. The relation between how I overconfidence and crash risk varies with business group affiliation was investigated. Results - The results showed that managerial overconfidence is positively related to crash risk. Specifically, the coefficient of OVERC is significantly positive, supporting the prediction. The results are strong and robust in non-chaebol firms. Conclusions - The results show that firms with overconfident managers are likely to experience stock price crashes. This study is related to past literature that examines the impact of managerial overconfidence on the stock market. This study contributes to the literature by examining whether overconfidence can explain a firm's future crashes.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.255-269
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• 2017

This article explores the analysis of longitudinal surveys in which same units are investigated on several occasions. Multivariate exponential ratio type estimator has been proposed for the estimation of the finite population median at the current occasion in two occasion longitudinal surveys. Information on several additional auxiliary variables, which are stable over time and readily available on both the occasions, has been utilized. Properties of the proposed multivariate estimator, including the optimum replacement strategy, are presented. The proposed multivariate estimator is compared with the sample median estimator when there is no matching from a previous occasion and with the exponential ratio type estimator in successive sampling when information is available on only one additional auxiliary variable. The merits of the proposed estimator are justified by empirical interpretations and validated by a simulation study with the help of some natural populations.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.51-56
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• 2006

Biases embedded in numerical weather precipitation forecasts by the RDAPS model was determined, quantified and corrected. The ultimate objective is to eventually enhance the reliability of reservoir operation by Korean Water Resources Corporation (KOWACO), which is based on precipitation-driven forecasts of stream flow. Statistical post-processing, so called MOS (Model Output Statistics) was applied to RDAPS to improve their performance. The Artificial Neural Nwetwork (ANN) model was applied for 4 cases of 'Probability of Precipitation (PoP) for wet and dry season' and 'Quantitative Precipitation Forecasts (QPF) for wet and dry season'. The reduction on the large systematic bias was especially remarkable. The performance of both networks may be improved by retraining, probably every month. In addition, it is expected that performance of the networks will improve once atmospheric profile data are incorporated in the analysis. The key to the optimal performance of ANN is to have a large data set relevant to the predictand variable. The more complex the process to be modeled by the ANN, the larger the data set needs to be.

• Near Infrared Analysis
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• v.1 no.1
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• pp.9-17
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• 2000

A variety of standardization methods between two near-infrared (NIR) spectrometers were investigated for the prediction of five constituents in trans-alkylation process. Spectra were collected by two different instruments (one is regarded as mater instrument, other on as slave instrument). Three well-known standardization methods of direct standardization (DS), piecewise direct standardization (PDS) and slope/bias correction of response variable were applied to trans-alkylation samples on the slave instrument. We have examined for a set of reliable standardization samples using smaller number of transfer samples in order to increase efficiency of standardization.