• 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.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.655-665
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• 2000

Several information criterions, Schwarz information criterion (SIC), Akaike information criterion (AIC), and the modified Akaike information criterion ($AIC_c$), are proposed to locate a change point in the multiple linear regression model. These methods are applied to a stock Exchange data set and compared to the results.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.2 no.1
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• pp.41-60
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• 1998

This paper derives a consistent and bias corrected extension of Akaike's Information Criterion (AIC), $AIC_{bc}$, based on Kullback-Leibler information. This criterion has terms that penalize the overparametrization more strongly for small and large samples than that of AIC. The overfitting problem of the asymptotically efficient model selection criteria for small and large samples will be overcome. The $AIC_{bc}$ also provides a consistent model order selection. Thus, it is widely applicable to data with small and/or large sample sizes, and to cases where the number of free parameters is a relatively large fraction of the sample size. Relationships with other model selection criteria such as $AIC_c$ of Hurvich, CAICF of Bozdogan and etc. are discussed. Empirical performances of the $AIC_{bc}$ are studied and discussed in better model order choices of a linear regression model using a Monte Carlo experiment.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.593-599
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• 2018

The onset times of P- and S-waves are important information to have reliable earthquake locations, 1D or 3D subsurface velocity structures, and other related studies in seismology. As the number of seismic stations increases significantly in recent years, it becomes a formidable task for network operators to pick phase arrivals manually. This study used a simple method to estimate additional P- and S-wave arrival times for local earthquakes when a priori information (event location and time) is available using the Akaike Information Criterion (AIC). We applied the AIC program to the earthquake data recorded at the seismic station located in Gyeongsan (DAG2). The comparisons of automatically estimated phase arrival times with manually picked onset times showed that 95.1% and 93.7% of P-wave and S-wave arrival time estimations, respectively, are less than 0.1 second difference. The higher percentage of agreement presented the method which can be successfully applied to large data sets recorded by high-density seismic arrays.

• Tunnel and Underground Space
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• v.21 no.5
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• pp.349-358
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• 2011

This paper introduces a new P wave arrival time determination algorithm of acoustic emission (AE) suitable to identify P waves with low signal-to-noise ratio generated in rock masses around the high-level radioactive waste disposal repositories. The algorithms adopted for this paper were amplitude threshold picker, Akaike Information Criterion (AIC), two step AIC, and Hinkley criterion. The elastic waves were generated by Pencil Lead Break test on a granite sample, then mixed with white noise to make it difficult to distinguish P wave artificially. The results obtained from amplitude threshold picker, AIC, and Hinkley criterion produced relatively large error due to the low signal-to-noise ratio. On the other hand, two step AIC algorithm provided the correct results regardless of white noise so that the accuracy of source localization was more improved and could be satisfied with the error range.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.10
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• pp.842-849
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• 1990

In this paper, a couple of well-known methods for determination of the number of source signals impinging on sersor array in array processing are introduced and compared in terms of estimation accuracy. The one is the procedure issued by Akaike(Akaike's Information Criterion : AIC) and the other one by Schwartz and Rissanen(Minimum Description Length:MDL). This paper demonstrates, through computer simulation, that the AIC is more reliable than the MDL in such troublesome cases as very closely spaced source signlas, very limited number of sensors in the array, finite data sequences and/or low Signal-to-Noise ratio(S/N).

• International Journal of Quality Innovation
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• v.2 no.1
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• pp.30-49
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• 2001

This paper considers a recent environment in the manufacturing process in which data in large amounts can be obtained on-line in real-time. Under this environment an on-line real-time Statistical Process Control (SPC) scheme equipped with detection of a process change, change-point estimation, and recognition of the change pattern is proposed. The proposed SPC scheme is composed of a Cusum chart, filtering methods and Akaike Information Criterion (AIC). We examine the performance of this scheme by Monte Carlo simulation and show its usefulness.

• Smart Structures and Systems
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• v.15 no.5
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• pp.1329-1344
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• 2015

The stiffness of a structure is one of several structural signals that are useful indicators of the amount of damage that has been done to the structure. To accurately estimate the stiffness, an equation of motion containing a stiffness parameter must first be established by expansion as a linear series model, a Taylor series model, or a power series model. The model is then used in multivariate autoregressive modeling to estimate the structural stiffness and compare it to the theoretical value. Stiffness assessment for modeling purposes typically involves the use of one of three statistical model refinement approaches, one of which is the efficient Akaike information criterion (AIC) proposed in this paper. If a newly added component of the model results in a decrease in the AIC value, compared to the value obtained with the previously added component(s), it is statistically justifiable to retain this new component; otherwise, it should be removed. This model refinement process is repeated until all of the components of the model are shown to be statistically justifiable. In this study, this model refinement approach was compared with the two other commonly used refinement approaches: principal component analysis (PCA) and principal component regression (PCR) combined with the AIC. The results indicate that the proposed AIC approach produces more accurate structural stiffness estimates than the other two approaches.

• Journal of the Korea Organic Resources Recycling Association
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• v.21 no.2
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• pp.88-96
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• 2013

The objective of this research was to evaluate the suitability of sigmoidal and firstorder kinetic equations for simulating the methane production from solid wastes. The sigmoidal kinetic equations used were modified Gompertz and Logistic equations. Statistical criteria used to evaluate equation performance were analysis of goodness-of-fit (Residual sum of squares, Root mean squared error and Akaike's Information Criterion). Akaike's Information Criterion (AIC) was employed to compare goodness-of-fit of equations with same and different numbers of parameters. RSS and RMSE were decreased for first-order kinetic equation with lag-phase time, compared to the first-order kinetic equation without lag-phase time. However, first-order kinetic equations had relatively higher AIC than the sigmoidal kinetic equations. It seemed that the sigmoidal kinetic equations had better goodness-of-fit than the first-order kinetic equations in order to simulate the methane production.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.921-927
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• 2012

Reliability-based design optimization (RBDO) can be used to determine the reliability of a system by means of probabilistic design criteria, i.e., the possibility of failure considering stochastic features of design variables and input parameters. To assure these criteria, various reliability analysis methods have been developed. Most of these methods assume that distribution functions are continuous. However, in real problems, because real data is often discrete in form, it is important to estimate the distributions for discrete information during reliability analysis. In this study, we employ the Akaike information criterion (AIC) method for reliability analysis to determine the best estimated distribution for discrete information and we suggest an RBDO method using AIC. Mathematical and engineering examples are illustrated to verify the proposed method.