• Journal of The Korean Astronomical Society
• /
• v.40 no.4
• /
• pp.99-106
• /
• 2007

Statistical analyses were performed to investigate the relative success and accuracy of daily maximum X-ray flux (MXF) predictions, using both multilinear regression and autoregressive time-series prediction methods. As input data for this work, we used 14 solar activity parameters recorded over the prior 2 year period (1989-1990) during the solar maximum of cycle 22. We applied the multilinear regression method to the following three groups: all 14 variables (G1), the 2 so-called 'cause' variables (sunspot complexity and sunspot group area) showing the highest correlations with MXF (G2), and the 2 'effect' variables (previous day MXF and the number of flares stronger than C4 class) showing the highest correlations with MXF (G3). For the advanced three days forecast, we applied the autoregressive timeseries method to the MXF data (GT). We compared the statistical results of these groups for 1991 data, using several statistical measures obtained from a $2{\times}2$ contingency table for forecasted versus observed events. As a result, we found that the statistical results of G1 and G3 are nearly the same each other and the 'effect' variables (G3) are more reliable predictors than the 'cause' variables. It is also found that while the statistical results of GT are a little worse than those of G1 for relatively weak flares, they are comparable to each other for strong flares. In general, all statistical measures show good predictions from all groups, provided that the flares are weaker than about M5 class; stronger flares rapidly become difficult to predict well, which is probably due to statistical inaccuracies arising from their rarity. Our statistical results of all flares except for the X-class flares were confirmed by Yates' $X^2$ statistical significance tests, at the 99% confidence level. Based on our model testing, we recommend a practical strategy for solar X-ray flare predictions.

• Communications for Statistical Applications and Methods
• /
• v.7 no.1
• /
• pp.257-268
• /
• 2000

As one of indirect ways to get an optimal answer for sensitive questions both lower and upper values are sometimes asked and collected. In this paper a statistical method is proposed to analyze this kind of data using graphics. This method could define each sample median and estimate an optimal value between lower and upper bounds. In particular we find that this method has similar explanations of an equilibrium price with demand and supply functions in Economics.

• Communications for Statistical Applications and Methods
• /
• v.13 no.1
• /
• pp.101-111
• /
• 2006

One of the most widely used method of handling missingness in microarray data is the kNN(k Nearest Neighborhood) method. Recently Li and Gui (2004) suggested, so called PCR(Partial Cox Regression) method which deals with censored survival times and microarray data efficiently via kNN imputation method. In this article, we try to show that the way to treat missingness eventually affects the further statistical analysis.

• Communications for Statistical Applications and Methods
• /
• v.6 no.3
• /
• pp.831-842
• /
• 1999

We propose a wavelet decomposition feature extraction method for the hand-written character recognition. Comparing the recognition rates of which methods with original image features and with selected features by the wavelet decomposition we study the characteristics of the proposed method. LDA(Linear Discriminant Analysis) QDA(Quadratic Discriminant Analysis) RDA(Regularized Discriminant Analysis) and NN(Neural network) are used for the calculation of recognition rates. 6000 hand-written numerals from CENPARMI at Concordia University are used for the experiment. We found that the set of significantly selected wavelet decomposed features generates higher recognition rate than the original image features.

• Communications for Statistical Applications and Methods
• /
• v.7 no.3
• /
• pp.687-698
• /
• 2000

An identification method is proposed in order to detect more than one outlying cells in multi-way contingency tables. The iterative proportional fitting method is applied to get expected values of several suspected outlying cells. Since the proposed method uses minimal sufficient statistics under quasi log-linear models, expected counts of outlying cells could be estimated under any hierarchical log-linear models. This method is an extension of the backwards-stepping method of Simonoff(1988) and requires les iteration to identify outlying cells.

• Proceedings of the Korean Statistical Society Conference
• /
• 2005.11a
• /
• pp.13-17
• /
• 2005

In recent years, many people use R as a statistics system. R is frequently updated by many R project teams. We are interested in the method of multiple outlier detection and know that R is not supplied the method of multiple outlier detection. In this talk, we review these procedures for detecting multiple outliers and provide more efficient procedures combined with direct methods and indirect methods using R.

• Communications for Statistical Applications and Methods
• /
• v.8 no.2
• /
• pp.367-374
• /
• 2001

In this paper, a method based on group divisible designs is presented for constructing some partial diallel crosses. We discuss in detail a particular inbred line, i.e., p lines divided into two groups with p$_1$ lines and p$_2$ lines. These designs are obtained by regarding the number of lines as treatments. In specially we study and compare the efficiency factors of the constructed partial diallel crosses with or without repeated blocks.

• Communications for Statistical Applications and Methods
• /
• v.26 no.1
• /
• pp.1-9
• /
• 2019

Various probabilistic methods have been proposed for using interpopulation allele frequency differences to infer the ethnic group of a DNA specimen. The selection of the statistical method is critical because the accuracy of the statistical classification results vary. For the ancestry classification, we proposed a new ancestry evaluation method that estimate the combined ethnicity index as well as compared its performance with various classical classification methods using two real data sets. We selected 13 SNPs that are useful for the inference of ethnic origin. These single nucleotide polymorphisms (SNPs) were analyzed by restriction fragment mass polymorphism assay and followed by classification among ethnic groups. We genotyped 400 individuals from four ethnic groups (100 African-American, 100 Caucasian, 100 Korean, and 100 Mexican-American) for 13 SNPs and allele frequencies that differed among the four ethnic groups. Additionally, we applied our new method to HapMap SNP genotypes for 1,011 samples from 4 populations (African, European, East Asian, and Central-South Asian). Our proposed method yielded the highest accuracy among statistical classification methods. Our ethnic group classification system based on the analysis of ancestry informative SNP markers can provide a useful statistical tool to identify ethnic groups.

• Nuclear Engineering and Technology
• /
• v.30 no.6
• /
• pp.617-627
• /
• 1998

A technically more direct Statistical Combination of Uncertainties (SCU) method, extended SCU (XSCU), was developed to statistically combine the uncertainties associated with the DNBR alarm setpoint and the DNBR trip setpoint of digital nuclear power plants. The Modified SCU (MSCU) method is currently used as the USNRC approved design method to perform the same function. In this study, the MSCU and XSCU methods were compared in terms of the total uncertainties, and the thermal margins to the DNBR alarm and trip setpoints. The MSCU method resulted in small total uncertainties due to large negative biases which are unphysical. The XSCU method gives virtually unbiased total uncertainties which are physically meaningful in order to represent the actual magnitude of the total uncertainties associated with the DNBR alarm and trip setpoints. But the thermal margins to the DNBR alarm and trip setpoints by the MSCU method agree with those by the XSCU method within allowable statistical Variations.

• Communications for Statistical Applications and Methods
• /
• v.12 no.1
• /
• pp.181-189
• /
• 2005

This paper suggest a method of inclusion probability proportional to size sampling that provides a non-negative and stable variance estimator. The sampling procedure is quite simple and flexible since a sampling design is easily obtained using mathematical programming. This scheme appears to be preferable to Nigam, Kumar and Gupta's (1984) method which uses a balanced incomplete block designs. A comparison is made with their method through an example in the literature.