• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.955-969
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• 2003

The classical Fourier analysis, which is the typical frequency domain approach, is used to detect periodic trends that are of the sinusoidal shape in time series data. In this article, using a sequence of periodic step functions, describes an adaptive Fourier series where the patterns may take general periodic shapes that include sinusoidal as a special case. The results, which extend both Fourier analysis and Walsh-Fourier analysis, are applies to investigate the shape of the periodic component. Through the real data, compare the goodness-of-fit of the model using two methods, the adaptive Fourier method which is proposed method in this paper and classical Fourier method.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.131-138
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• 1998

In usual statistical data analysis, we describe statistical data by exact values. However, in modem complex and large-scale systems, it is difficult to treat the systems using only exact data. In this paper, we define these data as fuzzy data(ie. Linguistic variable applied to make the member-ship function.) and Propose a new method to get an analysis of fuzzy survey data based on the maximum entropy Principle. Also, we propose a new method of discrimination by measuring distance between a distribution of the stable state and estimated distribution of the present state using the Kullback - Leibler information. Furthermore, we investigate the validity of our method by computer simulations under realistic situations.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.493-505
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• 1997

The analysis of binary data appears to many areas such as statistics, biometrics and econometrics. In many cases, data are often collected in which some observations are incomplete. Assume that the missing covariates are missing at random and the responses are completely observed. A method to Bayesian analysis of the binary regression model with incomplete data is presented. In particular, the desired marginal posterior moments of regression parameter are obtained using Meterpolis algorithm (Metropolis et al. 1953) within Gibbs sampler (Gelfand and Smith, 1990). Also, we compare logit model with probit model using Bayes factor which is approximated by importance sampling method. One example is presented.

• Korean Journal of Construction Engineering and Management
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• v.6 no.1 s.23
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• pp.73-79
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• 2005

This paper, introduces a Stochastic Project Scheduling Simulation system (SPSS III) developed by the author to predict a project completion probability in a certain time. The system integrates deterministic CPM, probabilistic PERT, and stochastic Discrete Event Simulation (DES) scheduling methods into one system. It implements automated statistical analysis methods for computing the minimum number of simulation runs, the significance of the difference between independent simulations, and the confidence interval for the mean project duration as well as sensitivity analysis method in What-if analyzer component. The SPSS 111 gives the several benefits to researchers in that it (1) complements PERT and Monte Carlo simulation by using stochastic activity durations via a web based JAVA simulation over the Internet, (2) provides a way to model a project network having different probability distribution functions, (3) implements statistical analyses method which enable to produce a reliable prediction of the probability of completing a project in a specified time, and (4) allows researchers to compare the outcome of CPM, PERT and DES under different variability or skewness in the activity duration data.

• Culinary science and hospitality research
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• v.21 no.6
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• pp.49-62
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• 2015

Give that statistical analysis is an essential component of foodservice-related research, the purpose of this review is to analyse research trends of statistical methods applied to foodservice-related research. To achieve these objective, this study carried out a content analysis on a total of 251 out of 415 research articles published in The Korean Journal of Culinary Research(TKJCR) from January 2010 to December 2013. Of the total 164 research articles focussing on natural science research, qualitative research, articles written in English were excluded from the scope of this study. The results of this study are as follows. First, it turned out that 269 research articles applied quantitative research methods, and only 10 articles applied qualitative research methods among the 279 research articles based on social science research methods. Second, 20 article (8.0%) among the 251 did not specify the statistical methods or computer programs that were used for statistical analysis. Third, it was found that 228 articles (90.8%) used the SPSS program for data analysis. Fourth, in terms of frequency of use, it was revealed frequency analysis was most used, followed in order by reliability analysis, exploratory factor analysis, correlation analysis, regression analysis, structural equation modeling, confirmatory factor analysis, t-test, variance analysis, and cross tabs analysis, However, 3 out of 56 research articles that used a t-test did not suggest a t-value. 10 out of 64 articles that used ANOVA and demonstrated a significant difference in between-group mean did not conducted post-hoc test. Therefore, the researchers with interest in foodservice fields need to keep in mind that choosing and applying the correct statistical technique both determine the value and the success or failure of a study. To enhance the value and success of a study, it is necessary to use the proper statistical technique in an efficient way in order to prevent statistical errors.

• Journal of Korea Water Resources Association
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• v.42 no.10
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• pp.795-807
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• 2009

In this study, homogeneity analysis was performed between rainfall observation data set of Chukwooki (CWK) and rainfall observation data set of modern rain gage (MRG) using Bootstrap method. Since traditional statistical homogeneity test method are validated only when distribution of their population is known, meteorological data which their statistical distributions of population are complicated were difficult to verify the homogeneity and there were plenty of room for doubt for their statistical significance using historical method. In this reason, in this study homogeneity test was evaluated between two data sets using bootstrap method which is not necessary to infer distribution of population. The test results show that there was an statistical homogeneity between CWK and MRG except for slight impact of climatical trend.

• Structural Engineering and Mechanics
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• v.69 no.4
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• pp.361-369
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• 2019

The determination of Paris' law parameters based on crack growth experiments is an important procedure of fatigue life assessment. However, it is a challenging task because it involves various sources of uncertainty. This paper proposes a novel probabilistic method, termed the S-N Paris law (SNPL) method, to quantify the uncertainties underlying the Paris' law parameters, by finding the best estimates of their statistical parameters from the S-N curve data using a Bayesian approach. Through a series of steps, the SNPL method determines the statistical parameters (e.g., mean and standard deviation) of the Paris' law parameters that will maximize the likelihood of observing the given S-N data. Because the SNPL method is based on a Bayesian approach, the prior statistical parameters can be updated when additional S-N test data are available. Thus, information on the Paris' law parameters can be obtained with greater reliability. The proposed method is tested by applying it to S-N curves of 40H steel and 20G steel, and the corresponding analysis results are in good agreement with the experimental observations.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.3
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• pp.193-202
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• 2014

This paper presents a new method of sampling the climatic data for infrared signature analysis. Historical hourly data from a stationary marine buoy of KMA(Korean Meteorological Administration) are used to select a small number of sample points (N=100) to adequately cover the range of statistics(PDF, CDF) displayed by the original data set (S=56,670). The method uses a coarse bin to subdivide the variable space ($3^5$=243 bins) to make sample points cover the original data range, and a single-point ranking system to select individual points so that uniform coverage (1/N = 0.01) is obtained for each variable. The principal component analysis is used to calculate a joint probability of the coupled climatic variables. The selected sample data show good agreement to the original data set in statistical distribution and they will be used for statistical analysis of infrared signature and susceptibility of naval ships.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.419-430
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• 2004

Since cDNA microarray experiments can monitor expression levels for thousands of genes simultaneously, the experimental designs and their analyzing methods are very important for successful analysis of microarray data. The loop design is discussed for selecting differentially expressed genes among several treatments and the analysis of variance method is introduced to normalize microarray data and provide estimates of the interesting quantities. MA-ANOVA is used to illustrate this method on a recently collected loop designed microarray data at Cancer Metastasis Research Center, Yonsei University.

• 모자간호학회지
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• v.4 no.1
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• pp.68-79
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• 1994

This study measured variables influencing the breast feeding patterns of lactating mothers over a 40 day period In 1993 in the Jeonla area. The Methodology used was a questionnaire covering 92 items based on statistical discriminant analysis. The results were as follows : The successful group was measured against the unsuccessful group over a 4month lactation period ; The successful group was measured over a 4month lactation period ; the unsuccessful less than 4month lactation period. Principal factor analysis was used to generate comparative data factors which were ; 1) nonunderstanding of mother's breast feeding, 2) physical and psychological stress, 3) insufficient milk supply, 4) mother's negative acceptance of baby, 5) lack of spousal support, 6) sore nipple and breast pain, 7) baby's negative acceptance, 8) lack of familial support, 9) baby's diarrhea and watery milk. Discriminant statistical analysis of sever factors included ; 1) insufficient milk supply 2) sore nipple and breast pain, 3) pre-natal planning of breast feeding method, 4) mother's occupation 5) breast feeding method of previous infant, 6) nipple type, and 7) infant birth order. This analysis predicted a 78.9% successful breast feeding. Criterion correlation analysis revealed ; D=-1.780+.165$\times$(Fac3)+.135$\times$(Fac6)+.927$\times$(prenatal planning of breast feeding method)+.900$\times$(mother's occupation)+.675$\times$ (breast feeding method of previous infant)+1.0l4$\times$(nipple type)+.378$\times$(infant birth order). We classified the unsuccessful group as more than .63937 and the successful group less than -.82742 of the D value obtained from the above criterion correlation in order to check the success or the non-success of breast feeding mothers. The rate of correct classification of the grouped cases employing a statistical discriminant analysis was significantly improved to 78.9% when these cases were compared with the actual grouped classification.