• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.425-433
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• 2011

Reject inference in credit scoring is a statistical approach to adjust for nonrandom sample bias due to rejected applicants. Function estimation approaches are based on the assumption that rejected applicants are not necessary to be included in the estimation, when the missing data mechanism is missing at random. On the other hand, the density estimation approach by using mixture models indicates that reject inference should include rejected applicants in the model. When mixture models are chosen for reject inference, it is often assumed that data follow a normal distribution. If data include missing values, an application of the normal mixture model to fully observed cases may cause another sample bias due to missing values. We extend reject inference by a multivariate normal mixture model to handle incomplete characteristic variables. A simulation study shows that inclusion of incomplete characteristic variables outperforms the function estimation approaches.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1017-1028
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• 2011

When it is not easy to decide the credit scoring for some loan applicants, credit evaluation is postponded and reserve to ask a specialist for further evaluation of undecided applicants. This undecided inference is one of problems that happen to most statistical models including the biostatistics and sportal statistics as well as credit evaluation area. In this work, the undecided inference is regarded as a missing data mechanism under the assumption of MNAR, and use the bivariate probit model which is one of sample selection models. Two undecided inference methods are proposed: one is to make use of characteristic variables to represent the state for decided applicants, and the other is that more accurate and additional informations are collected and apply these new variables. With an illustrated example, misclassification error rates for undecided and overall applicants are obtainded and compared according to various characteristic variables, undecided intervals, and thresholds. It is found that misclassification error rates could be reduced when the undecided interval is increased and more accurate information is put to model, since more accurate situation of decided applications are reflected in the bivariate probit model.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.613-635
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• 2001

This paper proposes a skewed multivariate probit model for analyzing a correlated binary response data with covariates. The proposed model is formulated by introducing an asymmetric link based upon a skewed multivariate normal distribution. The model connected to the asymmetric multivariate link, allows for flexible modeling of the correlation structure among binary responses and straightforward interpretation of the parameters. However, complex likelihood function of the model prevents us from fitting and analyzing the model analytically. Simulation-based Bayesian inference methodologies are provided to overcome the problem. We examine the suggested methods through two data sets in order to demonstrate their performances.

• Journal of The Korean Association For Science Education
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• v.22 no.2
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• pp.314-335
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• 2002

The purpose of this study was to investigate the effects of students' prior knowledge on scientific reasoning process performing a task of controlling variables with computer simulation and to identify a number of problems that students encounter in scientific discovery. Subjects for this study included 60 Korean students: 27 fifth-grade students from an elementary school; 33 seventh-grade students from a middle school. The sinking objects task involving multivariable causal inference was used. The task was presented as computer simulation. The fifth and seventh-grade students participated individually. A subject was interviewed individually while the investigating a scientific reasoning task. Interviews were videotaped for subsequent analysis. The results of this study indicated that students' prior knowledge had a strong effect on students' experimental intent; the majority of participants focused largely on demonstrating their prior knowledge or their current hypothesis. In addition, studnets' theories that were part of one's prior knowledge had significant impact on formulating hypotheses, testing hypothesis, evaluating evidence, and revising hypothesis. This study suggested that students' performance was characterized by tendencies to generate uninformative experiments, to make conclusion based on inconclusive or insufficient evidence, to ignore, reject, or reinterpret data inconsistent with their prior knowledge, to focus on causal factors and ignore noncausal factors, to have difficulty disconfirming prior knowledge, to have confirmation bias and inference bias (anchoring bias).

• (The)Study of the Eastern Classic
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• no.35
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• pp.275-295
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• 2009

The aim of this paper is to reject Graham's interpretation of bi (必) and xianzhi (先知) of Later Mohists' Mojing ("墨經") as logical necessity and a priori knowledge respectively. Graham's interpretations of them are based on his beliefs that Mojing distinguishes lun (論), the art of description from bian (辯), the art of inference in the Mohist disciplines and that the latter art should be seen as such a rigorous proof as Euclidean geometry even though it is not a Western formal logic. His beliefs also start from his distinguishing 'knowledge of names' from 'knowledge of conjunction of names and objects' according to the objects of knowledge. In my reading, the art of description and the art of inference, however, can't be sharply distinguished each other in Mojing and bi and xianzhi should be taken as suggesting both a normative necessity and an empirical necessity. A normative necessity is derived from 'normative theory of definition' which comes form the theory of rectification of names in China. The normative theory of definition, unlike the descriptive theory of definition, defines terms normatively rather than descriptively. For example, although such a definition of father, 'father is beneficient', has the form of being descriptive, but it actually is prescriptive and therefore means 'father should be beneficient'. Through this normative theory of definition, empirical knowledge, as long as it is a knowledge, is seen as necessary and so can't be wrong. To conclude, for Mohists an empirical knowledge is always a basis of an inferential knowledge or a priori knowledge, so Mohists' a priori knowledge is not really a fundamental knowledge and its necessity therefore is nothing but both a normative necessity and an empirical necessity.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.429-438
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• 2004

This Paper Presents the control structure for incoming traffic from arbitrary node to Provide admission control in policy-based W network management structure using fuzzy logic control approach. The proposed control structure uses scheme for deciding network resource allocation depending on requirements predefined-policies and network states. The proposed scheme enhances policy adapting methods of existing binary methods, and can use resource of network more effectively to provide adaptive admission control, according to the unpredictable network states for predefined QoS policies. Simulation results show that the proposed controller improves the ratio of packet rejection up to 26%, because it Performs the soft adaption based on the network states instead of accept/reject action in conventional CAC(Connection Admission Controller).

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.973-979
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• 2020

This study aims to objectively and quantitatively determine the social emotion of empathy by collecting pupillary response. 52 subjects (26 men and 26 women) voluntarily participated in the experiment. After the measurement of the reference of 30 seconds, the experiment was divided into the task of imitation and spontaneously self-expression. The two subjects were interacted through facial expressions, and the pupil images were recorded. The pupil data was processed through binarization and circular edge detection algorithm, and outlier detection and removal technique was used to reject eye-blinking. The pupil size according to the empathy was confirmed for statistical significance with test of normality and independent sample t-test. Statistical analysis results, the pupil size was significantly different between empathy (M ± SD = 0.050 ± 1.817)) and non-empathy (M ± SD = 1.659 ± 1.514) condition (t(92) = -4.629, p = 0.000). The rule of empathy according to the pupil size was defined through discriminant analysis, and the rule was verified (Estimation accuracy: 75%) new 12 subjects (6 men and 6 women, mean age ± SD = 22.84 ± 1.57 years). The method proposed in this study is non-contact camera technology and is expected to be utilized in various virtual reality with smart glasses.