• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.149-160
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• 2019

The electricity consumption time series data of 'A' University from July 2016 to June 2017 is analyzed via nonparametric functional data clustering since the time series data can be regarded as realization of continuous functions with dependency structure. We use a Bouveyron and Jacques (Advances in Data Analysis and Classification, 5, 4, 281-300, 2011) method based on model-based functional clustering with an FEM algorithm that assumes a Gaussian distribution on functional principal components. Clusterwise analysis is provided with cluster mean functions, densities and cluster profiles.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.481-488
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• 2016

This study is about comparing the mobile user experience of Facebook and Instagram which are most often used among the recent SNSs by the people in their 30s and under. This study analyzed the user experience level after dividing the user experience factors through the Creating Pleasurable Interfaces model, and suggested the mean analysis as well as the result of Wilcoxon rank test which is a nonparametric statistics method. As a result of study, the Display information visually factor in functional factor and the configuration of the main page in convenient factor were a statistically significant difference in the mobile user experience of Facebook and Instagram. It is expected that this study may help seeking the user experience factors to be promoted preferentially in a competitive situation through the statistical comparative evaluation of the experience of two SNS users.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.355-364
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• 2009

A simple method is proposed for constructing nonparametric confidence intervals for the difference or ratio of two median failure times. The method applies when clustered survival data with censoring is randomized either (I) under cluster randomization or (II) subunit randomization. This method is simple to calculate and is based on non-parametric density estimation. The proposed method is illustrated with the otology study data and HL-A antigen study data. Moreover, the simulation results are reported for practical sample sizes.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.315-324
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• 2003

Stream flow data was analyzed for determining the lowflow which is the standard for river maintenance flow. Lowflow quantiles were estimated based on the parametric and nonparametric methods and two methods were compared by Monte Carlo simulation study. As the results of the parametric method, three probability distributions such as gamma-2, lognormal-2 and Weibull-2, are selected as appropriate models for stream flow data of 13 stations in Han River Basins. According to simulation results, relative bias (RBIAS) and relative root mean square error (RRMSE) of the lowflow quantiles are the smallest when the applied and population models are the same. The fame statistical properties from the nonparametric models are good within the interpolation range. Among 7 bandwidth selectors used in this study, the RRMSEs of the Park and Marron method (PM) are the smallest while those of the Shoaler and Jones method (SJ) are the largest.

• The Korean Journal of Pain
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• v.23 no.1
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• pp.35-41
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• 2010

Background: Statistical analysis is essential in regard to obtaining objective reliability for medical research. However, medical researchers do not have enough statistical knowledge to properly analyze their study data. To help understand and potentially alleviate this problem, we have analyzed the statistical methods and errors of articles published in the Korean Journal of Pain (KJP), with the intention to improve the statistical quality of the journal. Methods: All the articles, except case reports and editorials, published from 2004 to 2008 in the KJP were reviewed. The types of applied statistical methods and errors in the articles were evaluated. Results: One hundred and thirty-nine original articles were reviewed. Inferential statistics and descriptive statistics were used in 119 papers and 20 papers, respectively. Only 20.9% of the papers were free from statistical errors. The most commonly adopted statistical method was the t-test (21.0%) followed by the chi-square test (15.9%). Errors of omission were encountered 101 times in 70 papers. Among the errors of omission, "no statistics used even though statistical methods were required" was the most common (40.6%). The errors of commission were encountered 165 times in 86 papers, among which "parametric inference for nonparametric data" was the most common (33.9%). Conclusions: We found various types of statistical errors in the articles published in the KJP. This suggests that meticulous attention should be given not only in the applying statistical procedures but also in the reviewing process to improve the value of the article.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.677-692
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• 2018

The total fertility rate of Korea was 1.05 in 2017, showing a return to the 1.08 level in the year 2005. 1.05 is a very low fertility level that is far from replacement level fertility or safety zone 1.5. The number may indicate a low fertility trap. It is therefore important to predict fertility than at any other time. In the meantime, we have predicted the age-specific fertility rate and total fertility rate by various statistical methods. When the data trend is disconnected or fluctuating, it applied a nonparametric method applying the smoothness and weight. In addition, the Bayesian method of using the pre-distribution of fertility rates in advanced countries with reference to the three-stage transition phenomenon have been applied. This paper examines which method is reasonable in terms of precision and feasibility by applying estimation, forecasting, and comparing the results of the recent variability of the Korean fertility rate with parametric, non-parametric and Bayesian methods. The results of the analysis showed that the total fertility rate was in the order of KOSTAT's total fertility rate, Bayesian, parametric and non-parametric method outcomes. Given the level of TFR 1.05 in 2017, the predicted total fertility rate derived from the parametric and nonparametric models is most reasonable. In addition, if a fertility rate data is highly complete and a quality is good, the parametric model approach is superior to other methods in terms of parameter estimation, calculation efficiency and goodness-of-fit.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.179-189
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• 2024

In multivariate regression with a high-dimensional response Y ∈ ℝ^r and a relatively low-dimensional predictor X ∈ ℝ^p (where r ≥ 2), the statistical analysis of such data presents significant challenges due to the exponential increase in the number of parameters as the dimension of the response grows. Most existing dimension reduction techniques primarily focus on reducing the dimension of the predictors (X), not the dimension of the response variable (Y). Yoo and Cook (2008) introduced a response dimension reduction method that preserves information about the conditional mean E(Y | X). Building upon this foundational work, Yoo (2018) proposed two semi-parametric methods, principal response reduction (PRR) and principal fitted response reduction (PFRR), then expanded these methods to unstructured principal fitted response reduction (UPFRR) (Yoo, 2019). This paper reviews these four response dimension reduction methodologies mentioned above. In addition, it introduces the implementation of the mbrdr package in R. The mbrdr is a unique tool in the R community, as it is specifically designed for response dimension reduction, setting it apart from existing dimension reduction packages that focus solely on predictors.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1049-1068
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• 2014

Autoregressive models are used to analyze an univariate time series data; however, these methods can be inappropriate when a structural break appears in a time series since they assume that a trend is consistent. Threshold autoregressive models (popular regime-switching models) have been proposed to address this problem. Recently, the models have been extended to two regime-switching models with delay parameter. We discuss two regime-switching threshold autoregressive models from a Bayesian point of view. For a Bayesian analysis, we consider a parametric threshold autoregressive model and a nonparametric threshold autoregressive model using Dirichlet process prior. The posterior distributions are derived and the posterior inferences is performed via Markov chain Monte Carlo method and based on two Bayesian threshold autoregressive models. We present a simulation study to compare the performance of the models. We also apply models to gross domestic product data of U.S.A and South Korea.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.807-822
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• 2016

The composite lognormal-GPD models (LN-GPD) enjoys both merits from log-normality for the body of distribution and GPD for the thick tailedness of the observation. However, in the estimation perspective, LN-GPD model performs poorly due to numerical instability. Therefore, a two-stage procedure, that estimates threshold first then estimates other parameters later, is a natural method to consider. This paper considers five nonparametric threshold estimation methods widely used in extreme value theory and compares their performance in LN-GPD parameter estimation. A simulation study reveals that simultaneous maximum likelihood estimation performs good in threshold estimation, but very poor in tail index estimation. However, the nonparametric method performs good in tail index estimation, but introduced bias in threshold estimation. Our method is illustrated to the service time of an Israel bank call center and shows that the LN-GPD model fits better than LN or GPD model alone.

• Osong Public Health and Research Perspectives
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• v.5 no.4
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• pp.187-192
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• 2014

Objectives: Measurement of the incidence of the human immunodeficiency virus (HIV) is very important for epidemiological studies. Here, we determined the recency period with the AxSYM avidity assay and the BED-capture enzyme immunoassay (BED-CEIA) in Korean seroconverters. Methods: Two hundred longitudinal specimens from 81 seroconverters with incident HIV infections that had been collected at the Korea National Institute of Health were subjected to the AxSYM avidity assay (cutoff = 0.8) and BED-CEIA (cutoff = 0.8). The statistical method used to estimate the recency period in recent HIV infections was nonparametric survival analyses. Sensitivity and specificity were calculated for 10-day increments from 120 days to 230 days to determine the recency period. Results: The mean recency period of the avidity assay and BED-CEIA using a survival method was 158 days [95% confidence interval (CI), 135-181 days] and 189 days (95% CI, 170-208 days), respectively. Based on the use of sensitivity and specificity, the mean recency period for the avidity assay and BED-CEIA was 150 days and 200 days, respectively. Conclusion: We determined the recency period to estimate HIV incidence in Korea. These data showed that the nonparametric survival analysis often led to shorter recency periods than analysis of sensitivity and specificity as a new method. These findings suggest that more data from seroconverters and other methodologies are needed to determine the recency period for estimating HIV incidence.