• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.427-432
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• 2005

This paper is concerned with a simultaneous design of TSK (Takagi-Sugeno-Kang)-linguistic fuzzy models with uncertain model output and the computationally efficient representation. For this purpose, we use the fundamental idea of linguistic models introduced by Pedrycz and develop their comprehensive design framework. The design process consists of several main phases such as (a) the automatic generation of the linguistic contexts by probabilistic distribution using CDF (conditional density function) and PDF (probability density function) (b) performing context-based fuzzy clustering preserving homogeneity based on the concept of fuzzy granulation (c) augment of bias term to compensate bias error (d) combination of TSK and linguistic context in the consequent part. Finally, we contrast the performance of the enhanced models with other fuzzy models for automobile MPG predication data and coagulant dosing process in a water purification plant.

• Korean Medical Education Review
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• v.12 no.1
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• pp.3-8
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• 2010

This study is designed to evaluate the effectiveness of teaching or studying programs, and thus to overcome the selectionbias in studies. Selection-bias derived from unobservable characteristics in the course of participants selection of the teaching or studying programs, in the case of cross-section data instrumental variable(IV) method and two stage least square estimation were suggested as an analysis tool. Panel data were analyzed by using both fixed effect in which individual effects are captured by intercept terms and random effect estimation where an unobserved effect can be characterized as being randomly drawn from a given distribution.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1557-1567
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• 2014

In this paper, I propose a general hypothesis that after the enactment of the Sarbanes-Oxley Act (SOA) financial statements convey more accurate and reliable corporate information to investors who in turn reflect such improvements in stock prices and test four practical hypotheses that simultaneously feature the degree of information asymmetry, forecast bias, and investor reaction to biased earnings information. The empirical results unanimously suggest that the post-SOA investors take advantage of the improvement in informational efficiency and accuracy and actively adjust for analyst forecast bias in earnings forecasts. The SOA indeed appears to achieve its primary goal of investor protection.

• MALSORI
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• no.58
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• pp.35-44
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• 2006

Maximum likelihood linear regression (MLLR) adaptation experiences severe performance degradation with very tiny amount of adaptation data. Eigenspace- based MLLR, as an alternative to MLLR for fast speaker adaptation, also has a weak point that it cannot deal with the mismatch between training and testing environments. In this paper, we propose a simultaneous fast speaker and environment adaptation based on eigenspace-based MLLR. We also extend the sub-stream based eigenspace-based MLLR to generalize the eigenspace-based MLLR with bias compensation. A vocabulary-independent word recognition experiment shows the proposed algorithm is superior to eigenspace-based MLLR regardless of the amount of adaptation data in diverse noisy environments. Especially, proposed sub-stream eigenspace-based MLLR with bias compensation yields 67% relative improvement with 10 adaptation words in 10 dB SNR environment, in comparison with the conventional eigenspace-based MLLR.

• Journal of Multimedia Information System
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• v.5 no.4
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• pp.245-256
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• 2018

This study estimated rainfall information more effectively by image signals through the information system of weather radar. Based on this, we suggest the way to estimate quantitative precipitation utilizing overlapped observation area of radars. We used the overlapped observation range of ground hyetometer observation network and radar observation network which are dense in our country. We chose the southern coast where precipitation entered from seaside is quite frequent and used Sungsan radar installed in Jeju island and Gudoksan radar installed in the southern coast area. We used the rainy season data generated in 2010 as the precipitation data. As a result, we found a reflectivity bias between two radar located in different area and developed the new quantitative precipitation estimation method using the bias. Estimated radar rainfall from this method showed the apt radar rainfall estimate than the other results from conventional method at overall rainfall field.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.319-323
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• 2008

"How does advertising affect sales?" is the fundamental issue of modern advertising research. There is an interesting issue for estimating carry over effects of advertising on sales, and the aggregated data biases exist in the duration of advertising effect. This research suggests a modified model at micro-data using Koyck model (Koyck 1954) by estimated model the aggregate data, and empirically shows the aggregated data bias. Our modified model with the aggregated level of actual data is more appropriate than the base model for micro-data. The result shows that it is very important to consider the disaggregated data level in the analysis of dynamic effects of adverting such as lagged effects.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.473-478
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• 2004

LIDAH data often include systematic errors, which should be removed by a calibration process. This paper proposes a robust approach to calibrating LIDAR data using natural surfaces as reference data. The uniqueness of this approach is to employ a sophisticated selection scheme so that only a portion of LIDAR points can be used to estimate the bias parameters generating the systematic errors. This approach was applied to calibrating simulated LIDAR data. The results show that the approach can successfully recover the bias parameters and calibrate the data with acceptable RMS errors. Particularly, the parameter recovery model can be easily extended to register image data with LIDAR data.

• Survey Research
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• v.10 no.1
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• pp.169-186
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• 2009

The propensity score method is used to minimize the bias level in social survey, which comes from nonresponse. The theoretical concept and the background of the propensity score method is discussed first. The propensity score method was first applied in the epidemiology observational study. I have summarized the process of the three propensity score methods that were used to reduce estimation bias in this study. Matching by propensity score is applied to the relatively large control group. Subclassification has the advantage of using whole control group data and regression adjustment is applied to multiple covariates as well as propensity score of each unit is computable and usable. Lastly, the application procedures of propensity score method to reduce the nonresponse bias is suggested and its applicability to real situation is reviewed with the existing data.

• The Korean Journal of Rehabilitation Nursing
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• v.6 no.2
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• pp.117-126
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• 2003

The purpose of this study was to identify the relationships of optimistic bias about gastric cancer, self-efficacy of healthy dietary behavior and unhealthy dietary habit in Korean adult men. The subjects were 394 men aged from 20 to 64 who lived in Seoul, Kyonggi Do, Kwang-Ju, Jeonnam Do. Data was collected by questionnaire surveys using convenient sampling. The instruments used for this study were extracted and modified from Lee's(2003). The collected data was analyzed using descriptive statistics, Pearson correlation coefficient, and stepwise multiple regression with SPSS/PC 10.0 version. Unhealthy dietary habit in adult men indicated a significantly negative correlation to optimistic bias about gastric cancer(r=-.159, p=.002) and self-efficacy of healthy dietary behavior(r=-.470, P=.000). The most significant predictors influencing unhealthy dietary habit in adult men were age and self-efficacy of healthy dietary behavior. The variance explained was about 24%. These results suggested that men of young age and lack of self-efficacy of healthy dietary behavior are likely to have unhealthy dietary behavior. Therefore, considering age and facilitating self-efficacy are needed in nursing education and intervention for dietary habit change.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.69-82
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• 2017

R-indicator (representativeness indicator) is used to check the representativeness of samples when non-responses occur. The representativeness is related with the accuracy of parameter estimator and the accuracy is related with bias of the estimator. Hence, unbiased estimator generates high accuracy. Therefore, high value of R-indicator guarantees the accuracy of parameter estimation with a small bias. R-indicator is calculated through propensity scores obtained by logit or probit modeling. In this paper we investigate the degree of relation between R-indicator and different non-response rates in strata using simulation studies. We also analyze a modified Korea Economic Census data for real data analysis.