• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4253-4264
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• 2022

Since the implementation of the phosphate project in Bled El-Hadba (BEH) deposit, western region of Tébessa, no detailed study has been conducted to assess the natural radioactivity distribution and the associated radiological risk parameter for this open-pit mine. For the sake of determining a credible premining reference database for the region of interest, 21 samples were collected from different geological layers of the above-mentioned deposit. Gamma Spectrometry was applied for measuring radioactivity using a high resolution HPGe semiconductor detector. The obtained activity results have shown a significant broad variation in the radioactive contents for the different phosphate samples. The total average concentrations (in Bq·kg^-1) for ²²⁶Ra, ²³⁸U, ²³⁵U, ²³²Th and ⁴⁰K computed for the different type of phosphate layers were found to be 570 ± 169, 788 ± 280, 52 ± 18, 66 ± 6 and 81 ± 18 respectively. The mean activity concentrations of the measured radionuclides were compared to other regional and worldwide deposits. The ratios between the detected radioisotopes have been calculated for spatial distribution of natural radionuclides in the study area. Based on the aforementioned activity concentrations, the corresponding radiation hazard parameters were assessed. Correlations between the obtained parameters were drawn and a multivariate statistical analysis (Pearson Correlation, Cluster and Factor analysis) was carried out in order to identify the existing relationships.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.267-275
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• 2011

According to recent studies, Bayesian information criteria(BIC) is proposed to determine the structural dimension of the central subspace through sliced inverse regression(SIR) with high-dimensional predictors. The BIC may be useful in K-means clustering inverse regression(KIR) with high-dimensional predictors. However, the direct application of the BIC to KIR may be problematic, because the slicing scheme in SIR is not the same as that of KIR. In this paper, we present empirical penalty term studies of BIC in KIR to identify the most appropriate one. Numerical studies and real data analysis are presented.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.1-8
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• 2004

We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Robin (1974), is extended to a more general case of non-monotone missing data. The proposed method is algebraically equivalent to the Newton-Raphson method for the observed likelihood, but avoids the burden of computing the first and the second partial derivatives of the observed likelihood Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method. A numerical example is also presented to illustrate the method.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.177-198
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• 2002

The estimation of variance components or variance ratios in linear model is an important issue in plant or animal breeding fields, and various estimation methods have been devised to estimate variance components or variance ratios. However, many traits of economic importance in those fields are observed as dichotomous or polychotomous outcomes. The usual estimation methods might not be appropriate for these cases. Recently threshold linear model is considered as an important tool to analyze discrete traits specially in animal breeding field. In this note, we consider a hierarchical Bayesian method for the threshold animal model. Gibbs sampler for making full Bayesian inferences about random effects as well as fixed effects is described to analyze jointly discrete traits and continuous traits. Numerical example of the model with two discrete ordered categorical traits, calving ease of calves from born by heifer and calving ease of calf from born by cow, and one normally distributed trait, birth weight, is provided.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• Communications for Statistical Applications and Methods
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• v.21 no.4
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• pp.317-325
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• 2014

Consider a nonlinear transform ${\Phi}(x)$ of x in $\mathbb{R}^p$ to Hilbert space H and assume that the dot product between ${\Phi}(x)$ and ${\Phi}(x^{\prime})$ in H is given by < ${\Phi}(x)$, ${\Phi}(x^{\prime})$ >= K(x, x'). The aim of this paper is to propose a mathematical technique to take screen shots of the multivariate dataset mapped to Hilbert space H, particularly suited to Gaussian kernel $K({\cdot},{\cdot})$, which is defined by $K(x,x^{\prime})={\exp}(-{\sigma}{\parallel}x-x^{\prime}{\parallel}^2)$, ${\sigma}$ > 0. Several numerical examples are given.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.695-704
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• 2008

The parallel coordinate plot is a graphical data analysis technique for plotting multivariate data. The parallel coordinate plot overcomes the visualization problem of the Cartesian coordinate system for dimensions greater than 4. But, using different ordering of coordinate axes in the parallel coordinate plot of the same data may make different interpretations. Hence, we can use the regular polyprism parallel coordinate plot as an alternative for overcoming the variable arrangement problem of the parallel coordinate plot.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.637-644
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• 2011

Partially ranked data refers to the situation in which there are p distinct objects; however each judge specifies only first s (s < p) choices. The group theoretic formulation for partially ranked data analysis was set up by Critchlow (1985). We propose a graphical method for partially ranked data by quantifying objects and judges. In a plot for judges, the interpoint distances can be interpreted as Spearman or Kendall distances between two rankings given by respective judges. Similarly, we also construct a plot for objects with a sensible relationship to the previous plot for judges. This study extends the Han and Huh (1995) quantification method of fully ranked data using Gabriel's (1971) biplot technique for multivariate data matrix.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.389-396
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• 2009

Approximations for the null distribution of a test statistic arising in multivariate analysis to test homogeneity of variances and a mixture of two beta distributions by making use of a product of beta baseline density function and a polynomial adjustment, so called beta-polynomial density approximant, are discussed. Explicit representations of density and distribution approximants of interest in each case can easily be obtained. Beta-polynomial density approximants produce good approximation over the entire range of the test statistic and also accommodate even the bimodal distribution using an artificial example of a mixture of two beta distributions.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.57-81
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• 1993

This paper is concerned with analyzing the bankruptcy prediction power of three methods : Multivariate Discriminant Analysis (MDA), Inductive Learning, Neural Network, MDA has been famous for its effectiveness for predicting bankrupcy in accounting fields. However, it requires rigorous statistical assumptions, so that violating one of the assumptions may result in biased outputs. In this respect, we alternatively propose the use of two AI models for bankrupcy prediction-inductive learning and neural network. To compare the performance of those two AI models with that of MDA, we have performed massive experiments with a number of Korean bankrupt-cases. Experimental results show that AI models proposed in this study can yield more robust and generalizing bankrupcy prediction than the conventional MDA can do.