• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.293-308
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• 1997

Sliced inverse regression is a method for reducing the dimension of the explanatory variable X without going through any parametric or nonparametric model fitting process. This method explores the simplicity of the inverse view of regression; that is, instead of regressing the univariate output varable y against the multivariate X, we regress X against y. In this article, we propose bivariate sliced inverse regression, whose method regress the multivariate X against the bivariate output variables $y_1, Y_2$. Bivariate sliced inverse regression estimates the e.d.r. directions of satisfying two generalized regression model simultaneously. For the application of bivariate sliced inverse regression, we decompose the output variable y into two variables, one variable y gained by projecting the output variable y onto the column space of X and the other variable r through projecting the output variable y onto the space orthogonal to the column space of X, respectively and then estimate the e.d.r. directions of the generalized regression model by utilize two variables simultaneously. As a result, bivariate sliced inverse regression of considering the variable y and r simultaneously estimates the e.d.r. directions efficiently and steadily when the regression model is linear, quadratic and nonlinear, respectively.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.53-63
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• 2008

It is commonly believed that disease of human or economic traits of livestock are caused not by single gene acting alone, but by multiple genes interacting with one an-other. This issue is difficult due to the limitations of parametric statistical method like as logistic regression for detection of gene effects that are dependent solely on interactions with other genes and with environmental exposures. Multifactor dimensionality reduction (MDR) nonparametric statistical method, to improve the identification of single nucleotide polymorphism (SNP) associated with the Hanwoo(Korean cattle) carcass cold weight, is applied and compared with ANOVA results.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.149-161
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• 2006

The most important component in decision tree algorithm is the rule for split variable selection. Many earlier algorithms such as CART and C4.5 use greedy search algorithm for variable selection. Recently, many methods were developed to cope with the weakness of greedy search algorithm. Most algorithms have different selection criteria depending on the type of variables: continuous or nominal. However, ordinal type variables are usually treated as continuous ones. This approach did not cause any trouble for the methods using greedy search algorithm. However, it may cause problems for the newer algorithms because they use statistical methods valid for continuous or nominal types only. In this paper, we propose a ordinal variable selection method that uses Cramer-von Mises testing procedure. We performed comparisons among CART, C4.5, QUEST, CRUISE, and the new method. It was shown that the new method has a good variable selection power for ordinal type variables.

• Journal of Wetlands Research
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• v.20 no.3
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• pp.201-209
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• 2018

In this study, the water quality index was calculated using the water quality monitoring data of the major tributaries of the Nakdong River and long-term trend analysis was performed to identify the tributaries requiring priority management. We used a Real-Time Water Quality Index method implemented by the Ministry of Environment. Linear regression as a parametric method and Mann-Kendall Test and Sen Slope Test as a nonparametric method were applied for the trend analysis. The water quality index of major tributaries except for Migeon2 and Seokyo2 was in the range below Fair grade and there were no significant trends for the rest of the sites except Bukan, Chennae, Hogye, Yongdeok. Therefore, in order to improve the water quality of the main stream, management of the tributaries should be preceded.

• Journal of Veterinary Clinics
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• v.24 no.4
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• pp.509-513
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• 2007

Parametric and nonparametric coupled with bootstrap simulation technique were used to reevaluate previously defined reference intervals of serum chemistry parameters. A population-based study was performed in 100 clinically healthy dogs that were retrieved from the medical records of Kangwon National University Animal Hospital during 2005-2006. Data were from 52 males and 48 females(1 to 8 years old, 2.2-5.8 kg of body weight). Chemistry parameters examined were blood urea nitrogen(BUN)(mg/dl), cholesterol(mg/dl), calcium(mg/dl), aspartate aminotransferase(AST)(U/L), alanine aminotransferase(ALT)(U/L), alkaline phosphatase(ALP)(U/L), and total protein(g/dl), and were measured by Ektachem DT 60 analyzer(Johnson & Johnson). All but calcium were highly skewed distributions. Outliers were commonly identified particularly in enzyme parameters, ranging 5-9% of the samples and the remaining were only 1-2%. Regardless of distribution type of each analyte, nonparametric methods showed better estimates for use in clinical chemistry compare to parametric methods. The mean and reference intervals estimated by nonparametric bootstrap methods of BUN, cholesterol, calcium, AST, ALT, ALP, and total protein were 14.7(7.0-24.2), 227.3(120.7-480.8), 10.9(8.1-12.5), 25.4(11.8-66.6), 25.5(11.7-68.9), 87.7(31.1-240.8), and 6.8(5.6-8.2), respectively. This study indicates that bootstrap methods could be a useful statistical method to establish population-based reference intervals of serum chemistry parameters, as it is often the case that many laboratory values do not confirm to a normal distribution. In addition, the results emphasize on the confidence intervals of the analytical parameters showing distribution-related variations.

• The Journal of Advanced Prosthodontics
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• v.9 no.3
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• pp.159-169
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• 2017

PURPOSE. The purposes of this study were to evaluate the marginal and internal gaps, and the potential clinical applications of three different methods of dental prostheses fabrication, and to compare the prostheses prepared using the silicone replica technique (SRT) and those prepared using the three-dimensional superimposition analysis (3DSA). MATERIALS AND METHODS. Five Pekkton, lithium disilicate, and zirconia crowns were each manufactured and tested using both the SRT and the two-dimensional section of the 3DSA. The data were analyzed with the nonparametric version of a two-way analysis of variance using rank-transformed values and the Tukey's post-hoc test (${\alpha}=.05$). RESULTS. Significant differences were observed between the fabrication methods in the marginal gap (P < .010), deep chamfer (P < .001), axial wall (P < .001), and occlusal area (P < .001). A significant difference in the occlusal area was found between the two measurement methods (P < .030), whereas no significant differences were found in the marginal gap (P > .350), deep chamfer (P > .719), and axial wall (P > .150). As the 3DSA method is three-dimensional, it allows for the measurement of arbitrary points. CONCLUSION. All of the three fabrication methods are valid for measuring clinical objectives because they produced prostheses within the clinically acceptable range. Furthermore, a three-dimensional superimposition analysis verification method such as the silicone replica technique is also applicable in clinical settings.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.689-700
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• 2017

This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.67-72
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• 2006

The aim of this paper is to discuss the effect of missing values in detecting differentially expressed genes in a cDNA microarray experiment in the context of a one sample problem. We conducted a cDNA micro array experiment to detect differentially expressed genes for the metastasis of colorectal cancer based on twenty patients who underwent liver resection due to liver metastasis from colorectal cancer. Total RNAs from metastatic liver tumor and adjacent normal liver tissue from a single patient were labeled with cy5 and cy3, respectively, and competitively hybridized to a cDNA microarray with 7775 human genes. We used $M=log_2(R/G)$ for the signal evaluation, where Rand G denoted the fluorescent intensities of Cy5 and Cy3 dyes, respectively. The statistical problem comprises a one sample test of testing E(M)=0 for each gene and involves multiple tests. The twenty cDNA microarray data would comprise a matrix of dimension 7775 by 20, if there were no missing values. However, missing values occur for various reasons. For each gene, the no missing proportion (NMP) was defined to be the proportion of non-missing values out of twenty. In detecting differentially expressed (DE) genes, we used the genes whose NMP is greater than or equal to 0.4 and then sequentially increased NMP by 0.1 for investigating its effect on the detection of DE genes. For each fixed NMP, we imputed the missing values with K-nearest neighbor method (K=10) and applied the nonparametric t-test of Dudoit et al. (2002), SAM by Tusher et al. (2001) and empirical Bayes procedure by $L\ddot{o}nnstedt$ and Speed (2002) to find out the effect of missing values in the final outcome. These three procedures yielded substantially agreeable result in detecting DE genes. Of these three procedures we used SAM for exploring the acceptable NMP level. The result showed that the optimum no missing proportion (NMP) found in this data set turned out to be 80%. It is more desirable to find the optimum level of NMP for each data set by applying the method described in this note, when the plot of (NMP, Number of overlapping genes) shows a turning point.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.695-708
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• 2011

Image fusion is the process of combining multiple images of the same scene into a single fused image with application to many fields, such as remote sensing, computer vision, robotics, medical imaging and military affairs. The widely used image fusion rules that use wavelet transform have been based on a simple comparison with the activity measures of local windows such as mean and standard deviation. In this case, information features from the original images are excluded in the fusion image and distorted fusion images are obtained for noisy images. In this paper, we propose the use of a nonparametric squared ranks test on the quality of variance for two samples in order to overcome the influence of the noise and guarantee the homogeneity of the fused image. We evaluate the method both quantitatively and qualitatively for image fusion as well as compare it to some existing fusion methods. Experimental results indicate that the proposed method is effective and provides satisfactory fusion results.

• Journal of Korean Neurosurgical Society
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• v.52 no.6
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• pp.523-527
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• 2012

Objective : The incidence of spontaneous spinal epidural hematoma (SSEH) is rare. Patients with SSEH, however, present disabling neurologic deficits. Clinical outcomes are variable among patients. To evaluate the adequate treatment method according to initial patients' neurological status and clinical outcome with comparison of variables affecting the clinical outcome. Methods : We included 15 patients suffered from SSEH. Patients were divided into two groups by treatment method. Initial neurological status and clinical outcomes were assessed by the American Spinal Injury Association (ASIA) impairment scale. Also sagittal hematoma location and length of involved segment was analyzed with magnetic resonance images. Other factors such as age, sex, premorbid medication and duration of hospital stay were reviewed with medical records. Nonparametric statistical analysis and subgroup analysis were performed to overcome small sample size. Results : Among fifteen patients, ten patients underwent decompressive surgery, and remaining five were treated with conservative therapy. Patients showed no different initial neurologic status between treatment groups. Initial neurologic status was strongly associated with neurological recovery (p=0.030). Factors that did not seem to affect clinical outcomes included : age, sex, length of the involved spinal segment, sagittal location of hematoma, premorbid medication of antiplatelets or anticoagulants, and treatment methods. Conclusion : For the management of SSEH, early decompressive surgery is usually recommended. However, conservative management can also be feasible in selective patients who present neurologic status as ASIA scale E or in whom early recovery of function has initiated with ASIA scale C or D.