• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.103-110
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• 1999

In this paper, we summarize some relative risk estimators under the two sample model with proportional hazard and examine the relative efficiencies of the nonparametric estimators relative to the maximum likelihood estimator of a parametric survival function under random censoring model by comparing their asymptotic variances.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.231-239
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• 2021

The purpose of this study is to analyze the effect of risk efficiency, financial decisions, and financial performance on firm value due to advances in financial reporting technology. This research was conducted on all banking sub-sector companies listed on the Indonesian capital market during a period of eight years, namely 2012-2019 which were selected using the purposive sampling method. The advancement of financial reporting technology is measured by two indicators based on the Internet financial reporting approach. Risk efficiency is measured using three indicators with a risk proxy relative efficiency approach using value at risk. Financial decisions are measured by two indicators that represent funding decisions and investment decisions. Financial performance is measured by two indicators with the profitability approach, and firm value is measured by two indicators based on the investor perception approach. The data analysis technique in this study used multivariate analysis with SEM-PLS. The empirical findings of this study are the advances in financial reporting technology, financial decisions, and risk-based efficiency value have a significant effect on firm value, while financial performance does not have a significant effect on firm value. Banking companies reduce risk to achieve efficiency and result in lower profits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.385-405
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• 2019

High Efficiency Video Coding (HEVC) enables significantly improved compression performance relative to existing standards. However, the advance also requires high computational complexity. To accelerate the intra prediction mode decision, a minimum risk Bayesian classification framework is introduced. The classifier selects a small number of candidate modes to be evaluated by a rate-distortion optimization process using the sum of absolute Hadamard transformed difference (SATD). Moreover, the proposed method provides a loss factor that is a good trade-off model between computational complexity and coding efficiency. Experimental results show that the proposed method achieves a 31.54% average reduction in the encoding run time with a negligible coding loss of 0.93% BD-rate relative to HEVC test model 16.6 for the Intra_Main common test condition.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.349-356
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• 2009

In this study, we suggest the estimation method of the relative risk for the unemployment statistics of a small area such as si, gun, gu in Korea. The considered method are the usual pooled estimator, weighted estimator with the inverse of log-variance as weights, and the Jackknife estimator. And we compare with the efficiency of the three estimators by estimating the bias and mean square errors using real data from the 2002 Economically Active Population Survey of Gyeonggi-do. We compute the unemployed rate of male and female in small areas, and then estimate the common relative risk for the unemployed rate between male and female. Also, the stability and reliability of the three estimators for the common relative risk was evaluated using the RB(relative bias) and the RRMSE(relative root mean square error) of these estimators. Finally, the Jackknife estimator turned out to be much more efficient than the other estimators.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.101-113
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• 2003

Quasi likelihood estimators defined by Wedderburn are derived for several nonlinear time series models. And also, the least squared estimator and Quasi-likelihood estimator are compared in sense of asymptotic relative efficiency at those models. Finally, we apply these estimations to a real data on exchanging rate and stock market prices.

• Journal of the Korean Statistical Society
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• v.35 no.1
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• pp.63-77
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• 2006

Maximum product of spacings estimator is proposed in this paper as a competent alternative of maximum likelihood estimator for the parameters of exponentiated-Weibull distribution, which does work even when the maximum likelihood estimator does not exist. In addition, a Bayes type estimator known as generalized maximum likelihood estimator is also obtained for both of the shape parameters of the aforesaid distribution. Though, the closed form solutions for these proposed estimators do not exist yet these can be obtained by simple appropriate numerical techniques. The relative performances of estimators are compared on the basis of their relative risk efficiencies obtained under symmetric and asymmetric losses. An example based on simulated data is considered for illustration.

• Health Policy and Management
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• v.30 no.2
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• pp.199-210
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• 2020

Background: Based on the importance of ceasing smoking programs to control the regional disparity of smoking behavior in Korea, this study aims to reveal the variation of smoke rate and determinants of it for 229 provinces. An evaluation of the relative efficiency of the cease smoking program under the consideration of regional characteristics was followed. Methods: The main sources of data are the Korean Statistical Information Service and a national survey on the expenditure of public health centers. Multivariate regression is performed to figure the determinants of regional variation of smoking rate. Based on the result of the regression model, clustering analysis was conducted to group 229 regions by their characteristics. Three clusters were generated. Using data envelopment analysis (DEA), relative efficiency scores are calculated. Results from the pooled model which put 229 provinces in one model to score relative efficiency were compared with the cluster-separated model of each cluster. Results: First, the maximum variation of the smoking rate was 16.9%p. Second, sex ration, the proportion of the elder, and high risk drinking alcohol behavior have a significant role in the regional variation of smoking. Third, the population and proportion of the elder are the main variables for clustering. Fourth, dissimilarity on the results of relative efficiency was found between the pooled model and cluster-separated model, especially for cluster 2. Conclusion: This study figured regional variation of smoking rate and its determinants on the regional level. Unconformity of the DEA results between different models implies the issues on regional features when the regional evaluation performed especially on the programs of public health centers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.151-161
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• 1999

For many decades, Deterministic DCF approach has been widely used to evaluate investment opportunities. Under new manufacturing conditions involving uncertainty and risk, the DCF approach is not appropriate. In DCF, Risk is incorporated in two ways: certainty equivalent method, risk adjusted discount rate. This paper proposes a determination method of the Risk Adjusted Discount Rate for economically decision making advanced manufacturing technologies. Conventional DCF techniques typically use discount rate which do not consider the difference in risk of differential investment options and periods. Due to their relative efficiency, advanced manufacturing technologies have different degree of risk. The risk differential of investments is included using $\beta$ coefficient of capital asset pricing model. The comparison between existing and proposed method investigated. The DCF model using proposed risk adjusted discount rate enable more reasonable evaluation of advanced manufacturing technologies.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.179-185
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• 1994

To test the equality of survival distributions in the presence of arbitrary right censorship, the choice of weights which are functions of the number of individuals at risk at the time of each death is very important in increasing the power of the test. In this paper a weight by probit scale is derived and the efficiencies relative to the other weight's are also investigated.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.1-19
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• 2023

Observational data with missing or incomplete data are common in biomedical research. Multiple imputation is an effective approach to handle missing data with the ability to decrease bias while increasing statistical power and efficiency. In recent years propensity score (PS) matching has been increasingly used in observational studies to estimate treatment effect as it can reduce confounding due to measured baseline covariates. In this paper, we describe in detail approaches to competing risk analysis in the setting of incomplete observational data when using PS matching. First, we used multiple imputation to impute several missing variables simultaneously, then conducted propensity-score matching to match statin-exposed patients with those unexposed. Afterwards, we assessed the effect of statin exposure on the risk of heart failure-related hospitalizations or emergency visits by estimating both relative and absolute effects. Collectively, we provided a general methodological framework to assess treatment effect in incomplete observational data. In addition, we presented a practical approach to produce overall cumulative incidence function (CIF) based on estimates from multiple imputed and PS-matched samples.