• Korean Journal of Culture and Social Issue
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• v.21 no.2
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• pp.177-203
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• 2015

The aims of this study were 1) to compare irrational gambling beliefs of gamblers and non-gamblers, 2) to investigate the role of cognitive error on winning probability thinking error, and 3) to examine the relationship between attributional bias and gambling behavior. A total of 248 subjects were recruited for this study. All subjects were classified into non-gamblers, social gamblers and pathological gamblers, and administered self-report questionnaires to measure irrational gambling beliefs, the probability inference error, the attriburional style, and the attributional bias. A pathological gambler group scored highest on irrational gambling beliefs, especially the overestimation of self-ability factor, and a social gambler group and a non-gambler group follow. All three groups scored higher on the magnification of gambling skills than the mean (4.0) of the scale. Pathological gamblers and social gamblers scored higher on the probability thinking error than non-gamblers. Pathological gamblers displayed higher external attribution, lower internal attribution in their daily life events and higher internal attribution in failure situation than social gamblers and non-gamblers. The results indicate that cognitive errors would be a factor that differentiates pathological gamblers from social gamblers and non-gamblers. In predicting gambling behaviors, overestimation of self-ability of irrational gambling beliefs, internal attribution in failure situation, external attribution in daily live event, and probability thinking error were identified as significant factors. It is concluded that a public education about common cognitive bias featured in gamblers might be important in prevention of pathological gambling behaviors.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.355-366
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• 2002

Nonparametric statistical inference for the parameter of logit model were examined. Usually nonparametric approach is milder than parametric approach based on normal theory assumption. We compared the two nonparametric methods for legit model, the bootstrap and random permutation in the sense of coverage probability. Monte Carlo simulation is conducted for small sample cases. Empirical power of hypothesis test and coverage probability for confidence interval estimation were presented for simple and multiple legit model respectively. An example were also introduced.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.39-43
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• 2000

분산 추정 및 신뢰구간 추정의 한 방법으로 널리 쓰이고 있는 붓스트랩 방법을 복합표본에 적용하는 방법에 대해 알아보았다. 복합 표본은 유한 모집단에서 추출되고 추출확률이 다르기 때문에 i.i.d. 표본에 기초하여 개발된 전통적인 붓스트랩 방법을 직접 적용하면 추론의 오류가 발생할 수 있다. 본 연구에서는 복원 확률비례표본과 랜덤그룹표본에 붓스트랩을 적용하는 방법을 알아보았다.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.2
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• pp.217-242
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• 2006

This Study reviewed 86 research articles using inferential statistics published in 2001-2004 in 4 korean core journals in the field of library and information science. Sampling methods, response rates and nonresponse bias, reliability test, and inferential statistic techniques used in the articles were critically reviewed and analyzed. Nonprobability sampling was mostly used. Average response rate was 74.47%. Parametric statistics were mostly used. Some misunderstandings in using each inferential statistics, especially Reliability Test, Multiple Regression, Factor Analysis, MDS, etc. were reported in this study.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.139-147
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• 2012

In this paper, we discuss the structure of biological knowledge discovery system based on text mining and automatic inference. Given a set of biology documents, the system produces a new hypothesis in an integrated manner. The text mining module of the system first extracts the 'event' information of predefined types from the documents. The inference module then produces a new hypothesis based on the extracted results. Such an integrated system can use information more up-to-date and diverse than other automatic knowledge discovery systems use. However, for the success of such an integrated system, the precision of the text mining module becomes crucial, as any hypothesis based on a single piece of false positive information would highly likely be erroneous. In this paper, we propose a probabilistic filtering method that filters out false positives from the extraction results. Our proposed method shows higher performance over an occurrence-based baseline method.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1289-1300
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• 2009

The double sampling scheme is effective in reducing the sampling cost. However, the doubly sampled data is contaminated by two types of error, namely false-positive and false-negative errors. These would make the statistical analysis more difficult, and it would require more sophisticate analysis tools. For instance, the Wald method for the interval estimation of a proportion would not work well. In fact, it is well known that the Wald confidence interval behaves very poorly in many sampling schemes. In this note, the property of the Wald interval is investigated in terms of the coverage probability and the expected width. An alternative confidence interval based on the Agresti-Coull's approach is recommended.

• Survey Research
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• v.1 no.1
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• pp.109-123
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• 2000

In order to make statistically valid inferences for survey data based on complex probability sample designs, survey researchers must incorporate the sample design in the data analysis If this in not the case the variance estimates of survey statistics derived under the usual simple random sampling assumptions from an infinite population generally underestimate the true variance, which results in high Type l error level. In this article we introduce new software packages dedicated to analyze complex survey data In particular, we summarize analysis capabilities on SUDAAN Version 7.5 and SAS Version 8.

• Korean Journal of Culture and Social Issue
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• v.19 no.2
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• pp.159-178
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• 2013

The purpose of this study was to 1) replicate the Wells effect(i.e., reluctance to rule against the Defendant solely on the basis of probabilistic evidence) in Korea and 2) examine the validity of an Alternative explanation(i.e., perception of risk of wrong verdict). In study 1(n=46), mock jurors in the tire-tracks condition were reluctant to rule against the defendant based on their perceived probability and this pattern was not resulted in the tire-tracks-belief condition. Therefore, the Wells effect was replicated in Korea. In study 2(n=70), we manipulated the participants' perception of risk of wrong verdict. That is, participants who were assigned in the high risk perception of wrong verdict were informed that if the defendant were found guilty, the defendant would get considerable demage both in finance and reputation of the company. Participants in the low risk perception of wrong verdict condition were informed that these demage would not be great. The results revealed that the Wells effect was pronounced in the high risk perception of wrong verdict condition. That is, participants were more reluctant to rule against the defendant when they perceive the significance of the result of wrong verdict as high. Limitations of the study and the directions for future study were discussed.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.9-16
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• 2019

Companies that want to improve their manufacturing processes have recently introduced the smart factory, which is particularly noticeable. The ultimate goal is to maximize the area of the smart factory that performs the process of the production facility completely with minimal manual control and to minimize errors of reasoning. This research is a part of a project for unmanned production, management, packaging, and delivery management and the detection of the start point of rebars to perform the automatic calibration of the rollers through the tracking of the automated facilities of unmanned production. It must meet the requirement to accurately track the position from the start point to the end point. In order to improve the tracking performance, it is important to set the accurate start point. However, the probability of tracking errors is high depending on environments such as illumination and dust through the conventional time-based detection method. In this paper, we propose a starting point detection method using the average brightness change of high speed IR camera to reduce the errors according to the environments, As a result, its performance is improved by more than 15%.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.581-594
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• 2013

A group sequential design can end a clinical trial early if a confirmed efficacy or a futility of study medication is found during clinical trials. Adaptation can adjust the design of clinical trials based on accumulated data. The key to this methodology is considered to control the overall type 1 error rate while maintaining the integrity of clinical trials. The estimation would be more complex and the sample size calculation will be more difficult if the clinical trials have repeated measurement data. Lee et al. (2002) suggested a repeated observation case by using the independent increments properties of the interim test statistics and investigated the properties of the proposed confidence interval based on the stage-wise ordering. This study extend Lee et al. (2002) to adaptive group sequential design. We suggest test statistics for the adaptation as redesigning the second stage of clinical trials and induce the stage-wise confidence interval of parameter of interests. The simulation will help to confirm the suggested method.