• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.119-133
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• 2017

Corrected Akaike's information criterion (AICc) is known to have better finite sample properties. However, Akaike's information criterion (AIC) is still widely used to select an optimal prediction model among several candidate models due to of a lack of research on benefits obtained using AICc. In this paper, we compare the performance of AIC and AICc through numerical simulations and confirm the advantage of using AICc. In addition, we also consider the performance of quasi Akaike's information criterion (QAIC) and the corrected quasi Akaike's information criterion (QAICc) for binomial and Poisson data under overdispersion phenomenon.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.1-19
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• 2023

This study is a study on the effect of technical readiness factors on counselors' intention to use when applying AICC. AICC counselors experience improved customer service and emotional stability by receiving various monitor notification window services based on artificial intelligence algorithms such as customer counseling history, prohibited word control system, and customized counseling system. Accordingly, this study tried to verify using factors derived from technology readiness theory and technology acceptance theory among the factors affecting the intention to continue using AICC provided to counselors. To verify the research hypothesis, the causal relationship between variables such as Optimism, Innovativeness, Discomfort, Insecurity, and Technology Acceptance Theory, such as Team Support, Ease of Usage, and Innovation Resistance, was verified. As a result of empirical analysis, first, it was verified that Optimism has a positive (+) effect on Team Support and Ease of Usage, and Discomfort and Insecurity have a negative (-) effect on Ease of Usage and Team Support. Second, it was confirmed that Team Support and Ease of Usage had a positive effect on the Intention to use AICC. Based on the above empirical analysis results, the concepts of Technical Readiness were clearly proved, and in practical terms, AICC helped inquiry, quality evaluation, recording, and management of counseling history, ultimately increased corporate work efficiency.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.6
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• pp.175-186
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• 1996

In Advanced Vehicle Control System(AVCS), Autonomous Intelligent Cruise Control(AICC) is generally understood to be a system that can be achieved in the near future without the demanding infrastructure components and technoloties. AICC is an automatic vehicle following system with no human engagement in the longitudinal vehicle direction. This paper presents a fuzzy control algorithm to develop the AICC system. The control performance was studied information of vehicles using computer simulations. The most improtant aspects of the work reported here are the adoption of the fuzzy adaptive control law, and the use of filtering concept to reduce the slinky effects that may appear in a formation of vehicles equipped with AICC systems. The simulation results demonstrate the effectiveness of the fuzzy adaptive AICC system and its beneficial effects on traffic flow.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.143-143
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• 2015

기존의 빈도해석에서는 자료의 정상성을 가정하며, 이에 따라 적정모형 선정 시에 $x^2$ 검정이나 PPCC(Probability Plot Correlation Coefficient)검정과 같은 적합도 검정방법을 사용한다. 하지만 자료에서 경향성이 나타나거나 평균, 분산, 매개변수 등이 시간에 따라 변하는 등의 비정상성 현상들이 관측됨에 따라 비정상성 빈도해석에 관한 연구들이 활발히 진행되고 있다. 비정상성 빈도해석에서는 시간항과 같은 공변량이 포함된 매개변수를 가지는 비정상성 모형을 적용하게 되는데, 시간에 따라 매개변수가 계속 변하므로 매개변수에 따라 검정통계량이 고정되어 있는 기존의 적합도 검정방법의 적용이 어렵다. 따라서 비정상성 빈도해석의 적정 모형 선정에 적용할 수 있는 방법으로 최우도 함수에 기반한 모형 평가 방법인 AIC와 BIC가 추천되고 있으며 자료길이가 충분하지 않은 경우에는 AIC 대신하여 AICc의 사용이 추천되고 있다. 본 연구에서는 극치사상을 나타내는데 적합한 분포형인 GEV분포형의 위치, 규모 매개변수를 시간항으로 나타낸 다양한 비정상성 GEV모형에 대하여 Monte-Carlo 모의실험을 통해 AICc와 BIC의 적용성을 검토하였으며, 비정상성이 관측되는 실측 자료에 적용해보았다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.268-275
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• 2005

Early predictions of crop yields call provide information to producers to take advantages of opportunities into market places, to assess national food security, and to provide early food shortage warning. The objectives of this study were to identify the most useful parameters for estimating yields and to compare two model selection methods for finding the 'best' model developed by multiple linear regression. This research was conducted in two 65ha corn/soybean rotation fields located in east central South Dakota. Data used to develop models were small temporal variability information (STVI: elevation, apparent electrical conductivity $(EC_a)$, slope), large temporal variability information (LTVI : inorganic N, Olsen P, soil moisture), and remote sensing information (green, red, and NIR bands and normalized difference vegetation index (NDVI), green normalized difference vegetation index (GDVI)). Second order Akaike's Information Criterion (AICc) and Stepwise multiple regression were used to develop the best-fitting equations in each system (information groups). The models with $\Delta_i\leq2$ were selected and 22 and 37 models were selected at Moody and Brookings, respectively. Based on the results, the most useful variables to estimate corn yield were different in each field. Elevation and $EC_a$ were consistently the most useful variables in both fields and most of the systems. Model selection was different in each field. Different number of variables were selected in different fields. These results might be contributed to different landscapes and management histories of the study fields. The most common variables selected by AICc and Stepwise were different. In validation, Stepwise was slightly better than AICc at Moody and at Brookings AICc was slightly better than Stepwise. Results suggest that the Alec approach can be used to identify the most useful information and select the 'best' yield models for production fields.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.73-77
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• 2005

본 논문은 시계열에 내재된 설${\cdot}$추석 등 음력 명절효과의 존재를 검정하기 위해 RegARIMA 모형의 잔차에 대한 t-검정 통계량을 제시하였으며 Box-plot에 의한 그래프적 진단을 시도하였다. 제시된 t-검정 결과를 X-12-ARIMA의 AICC-사전검정 및 RegARIMA 모형에 의해 추정된 명절효과 회귀계수의 t-값과 비교하였다. 사용된 명절효과 변수는 Bell과 Hillmer(1983)의 명절효과 변수이다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.4
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• pp.405-415
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• 2000

Time series of wind-induced pressure on a structure are modeled using autoregressive moving average (ARMA) model. In an AR process, the current value of the time series is expressed in terms of a finite, linear combination of the previous values and a white noise. In a MA process, the value of the time series is linearly dependent on a finite number of the previous white noises. The ARMA process is a combination of the AR and MA processes. In this paper, the ARMA models with several different combinations of the AR and MA orders are fitted to the wind-induced pressure time series, and the procedure to select the most appropriate ARMA model to represent the data is described. The maximum likelihood method is used to estimate the model parameters, and the AICC model selection criterion is employed in the optimization of the model order, which is assumed to be a measure of the temporal complexity of the pressure time series. The goodness of fit of the model is examined using the LBP test. It is shown that AR processes adequately fit wind pressure time series.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.645-652
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• 2016

The statistical modeling of input random variables is necessary in reliability analysis, reliability-based design optimization, and statistical validation and calibration of analysis models of mechanical systems. In statistical modeling methods, there are the Akaike Information Criterion (AIC), AIC correction (AICc), Bayesian Information Criterion, Maximum Likelihood Estimation (MLE), and Bayesian method. Those methods basically select the best fitted distribution among candidate models by calculating their likelihood function values from a given data set. The number of data or parameters in some methods are considered to identify the distribution types. On the other hand, the engineers in a real field have difficulties in selecting the statistical modeling method to obtain a statistical model of the experimental data because of a lack of knowledge of those methods. In this study, commonly used statistical modeling methods were compared using statistical simulation tests. Their advantages and disadvantages were then analyzed. In the simulation tests, various types of distribution were assumed as populations and the samples were generated randomly from them with different sample sizes. Real engineering data were used to verify each statistical modeling method.

• The Journal of Korean Association of Computer Education
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• v.6 no.1
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• pp.19-28
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• 2003

In this paper, we propose a development methodology based on the component architecture to develop the LMS that follows ADL's SCORM standard, which is interested internally and externally in the e-learning contents standardization research. In like manner, we designed the SCORM core module and implemented the EJB component based on J2EE through the sugested methodology for the conversion of SCORM Conformance LMS, from iOneLMS which was developed based on distributed object component like JSP, EJB, etc. The SCORM component modules were implemented by three modules - content importing, course registering, and taking course and gathering the learning data. In the process of implementation, we used the part of the class library that was supported by the ADL and AICC again.

• Genomics & Informatics
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• v.18 no.3
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• pp.30.1-30.7
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• 2020

The novel coronavirus pandemic that has originated from China and spread throughout the world in three months. Genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) predecessor, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) play an important role in understanding the concept of genetic variation. In this paper, the genomic data accessed from National Center for Biotechnology Information (NCBI) through Molecular Evolutionary Genetic Analysis (MEGA) for statistical analysis. Firstly, the Bayesian information criterion (BIC) and Akaike information criterion (AICc) are used to evaluate the best substitution pattern. Secondly, the maximum likelihood method used to estimate of transition/transversions (R) through Kimura-2, Tamura-3, Hasegawa-Kishino-Yano, and Tamura-Nei nucleotide substitutions model. Thirdly and finally nucleotide frequencies computed based on genomic data of NCBI. The results indicate that general times reversible model has the lowest BIC and AICc score 347,394 and 347,287, respectively. The transition/transversions bias for nucleotide substitutions models varies from 0.56 to 0.59 in MEGA output. The average nitrogenous bases frequency of U, C, A, and G are 31.74, 19.48, 28.04, and 20.74, respectively in percentages. Overall the genomic data analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV highlights the close genetic relationship.