• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.289-298
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• 1999

We consider a minimax approach to make a design robust to many types or uncertainty arising in reality when dealing with non-normal linear models. We try to build a design to protect against the worst case, i.e. to improve the "efficiency" of the worst situation that can happen. In this paper, we especially deal with the generalized linear model. It is a known fact that the generalized linear model is a universal approach, an extension of the normal linear regression model to cover other distributions. Therefore, the optimal design for the generalized linear model has very similar properties as the normal linear model except that it has some special characteristics. Uncertainties regarding the unknown parameters, link function, and the model structure are discussed. We show that the suggested approach is proven to be highly efficient and useful in practice. In the meantime, a computer algorithm is discussed and a conclusion follows.

• Journal of Biosystems Engineering
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• v.41 no.1
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• pp.51-59
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• 2016

Purpose: This study examined the performance of two spectroscopy methods and multivariate classification methods to discriminate viable pepper seeds from their non-viable counterparts. Methods: A classification model for viable seeds was developed using partial least square discrimination analysis (PLS-DA) with Fourier transform near-infrared (FT-NIR) and Raman spectroscopic data in the range of $9080-4150cm^{-1}$ (1400-2400 nm) and $1800-970cm^{-1}$, respectively. The datasets were divided into 70% to calibration and 30% to validation. To reduce noise from the spectra and compare the classification results, preprocessing methods, such as mean, maximum, and range normalization, multivariate scattering correction, standard normal variate, and $1^{st}$ and $2^{nd}$ derivatives with the Savitzky-Golay algorithm were used. Results: The classification accuracies for calibration using FT-NIR and Raman spectroscopy were both 99% with first derivative, whereas the validation accuracies were 90.5% with both multivariate scattering correction and standard normal variate, and 96.4% with the raw data (non-preprocessed data). Conclusions: These results indicate that FT-NIR and Raman spectroscopy are valuable tools for a feasible classification and evaluation of viable pepper seeds by providing useful information based on PLS-DA and the threshold value.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.1073-1080
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• 1996

The impact of the input and output probability distributions on the performance of neural networks to forecast two year peak stream flow (cubic meters per second) is examined for two major river basins of the US. The neural network input consisted of drainage area(square kilometers ) and elevation (meters). When data are normally distributed , the neural networks predict much better than when the data are non-normal and have larger tails in their distributions.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.321-338
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• 2009

The objective of this paper is to compare spatial prediction capabilities of univariate kriging algorithms for generating GIS-based thematic maps from ground survey data with asymmetric distributions. Four univariate kriging algorithms including traditional ordinary kriging, three non-linear transform-based kriging algorithms such as log-normal kriging, multi-Gaussian kriging and indicator kriging are applied for spatial interpolation of geochemical As and Pb elements. Cross validation based on a leave-one-out approach is applied and then prediction errors are computed. The impact of the sampling density of the ground survey data on the prediction errors are also investigated. Through the case study, indicator kriging showed the smallest prediction errors and superior prediction capabilities of very low and very high values. Other non-linear transform based kriging algorithms yielded better prediction capabilities than traditional ordinary kriging. Log-normal kriging which has been widely applied, however, produced biased estimation results (overall, overestimation). It is expected that such quantitative comparison results would be effectively used for the selection of an optimal kriging algorithm for spatial interpolation of ground survey data with asymmetric distributions.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2941-2959
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• 2022

The fire brigade non-suppression probability model is a major factor that should be considered in evaluating fire-induced risk through fire probabilistic risk assessment (PRA), and also uncertainty is a critical consideration in support of risk-informed performance-based (RIPB) fire protection decision-making. This study developed an optimal integrated probabilistic fire brigade non-suppression model considering uncertainty of parameters based on the Bayesian Markov Chain Monte Carlo (MCMC) approach on electrical fire which is one of the most risk significant contributors. The result shows that the log-normal probability model with a location parameter (µ) of 2.063 and a scale parameter (σ) of 1.879 is best fitting to the actual fire experience data. It gives optimal model adequacy performance with Bayesian information criterion (BIC) of -1601.766, residual sum of squares (RSS) of 2.51E-04, and mean squared error (MSE) of 2.08E-06. This optimal log-normal model shows the better performance of the model adequacy than the exponential probability model suggested in the current fire PRA methodology, with a decrease of 17.3% in BIC, 85.3% in RSS, and 85.3% in MSE. The outcomes of this study are expected to contribute to the improvement and securement of fire PRA realism in the support of decision-making for RIPB fire protection programs.

• Korean Journal of Social Welfare
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• v.68 no.3
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• pp.73-103
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• 2016

In this study, it investigates the life of normal adolescents who have parents with intellectual disability and their difficulties which suggested social welfare meanings of this study. In order to conduct wide and in-depth analysis on cases by utilizing the characteristics of qualitative case studies, it describes and analyzes the intellectual disability parents' normal children in detail from the viewpoint of an insider through in-depth interviews, various sources and diverse data collecting methods. As for the subject of this study, both parents should be persons with intellectual disability and their child shall be non-disabled and at least a high school student or older. Through the intentional sampling, five late adolescents who were in high school, all males participated in the study. The data collection process had been conducted from January 2014 to May, which is commonly utilized for qualitative case studies, and comparative analysis between cases were practiced for analysis. For credibility of the research results, it obtained severity at each stage by meeting the standard. The analysis results were largely divided into "growth story of non-disabled adolescents" and "life of non-disabled adolescents". Nine upper categories analyzed the common features in each case. The nine categories were "no one tells me to study", "advance while learning the sense of academic achievement", "hide into my own space", "having to grown up early", "different parents but same love", "relatives raised me", "have a friend who accepts me as I am", "being pressed by poverty", and "standing on a knife edge of being hurt and taking heart". Based on the in-depth research on normal teens that have intellectually disabled parents, theoretically speaking, this study expanded the prospect of study on intellectually disabled to their normal, intellectual teenage children. As for practical significance, understanding their parents' intellectual disability, parenting technique training, case management from the community level is suggested. Rregular real condition research of the families, allowance system for economic support et al. is suggested in policy aspect.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.3
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• pp.77-86
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• 2000

The main objectives of this dissertation is to propose a forth generation index C for the case where the target value T is not equal to the midpoint of the specification limits (i.e. asymmetric tolerances), and show that this index is more sensitive compared to the standard PCI's in detacting small shifts of the process mean from the target value. In conclusion, in this dissertation , a new methods for estimating a measure of process capability for non-normally distributed variable data is proposed using the percentage nonconforming.

• Journal of Oral Medicine and Pain
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• v.23 no.1
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• pp.65-73
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• 1998

This study evaluated whether substantial airflow resistance changes occurred by changing jaw position in normal and snoring subjects. A case-control design was utilized to assess group differences. Subjects included 11 snoring patients and 10 non-snoring subjects. Airway resistance was assessed using a whole body plethysmograph. Subjects in this study had their mouth opening standardized to a position of 7 mm of vertical separation and the resistance was measured under the following conditions; normal jaw position and 2/3 maximum protrusive jaw position. The results were as follows : 1. The airway resistance was higher in snoring group than in non-snoring group. 2. Both groups had a significant decrease in their airflow resistance upon jaw protrusion. In conclusion, these data document that airflow resistance can be significantly influenced by jaw positioning. Moving the jaw in a protrusive position produced reduction of resistance.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.273-293
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• 2019

We consider the problem of model selection in multiple linear regression with outliers and non-normal error distributions. In this article, the robust model selection criterion is proposed based on the robust estimation method with the least absolute deviation (LAD). The proposed criterion is shown to be consistent. We suggest proposed criterion based algorithms that are suitable for a large number of predictors in the model. These algorithms select only relevant predictor variables with probability one for large sample sizes. An exhaustive simulation study shows that the criterion performs well. However, the proposed criterion is applied to a real data set to examine its applicability. The simulation results show the proficiency of algorithms in the presence of outliers, non-normal distribution, and multicollinearity.