• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.141-148
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• 2020

We consider estimating the proportion of true null hypotheses in multiple testing problems. A traditional multiple testing rate, family-wise error rate is too conservative and old to control type I error in multiple testing setups; however, false discovery rate (FDR) has received significant attention in many research areas such as GWAS data, FMRI data, and signal processing. Identify differentially expressed genes in microarray studies involves estimating the proportion of true null hypotheses in FDR procedures. However, we need to account for unknown dependence structures among genes in microarray data in order to estimate the proportion of true null hypothesis since the genuine dependence structure of microarray data is unknown. We compare various procedures in simulation data and real microarray data. We consider a hidden Markov model for simulated data with dependency. Cai procedure (2007) and a sliding linear model procedure (2011) have a relatively smaller bias and standard errors, being more proper for estimating the proportion of true null hypotheses in simulated data under various setups. Real data analysis shows that 5 estimation procedures among 9 procedures have almost similar values of the estimated proportion of true null hypotheses in microarray data.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.217-234
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• 2018

Copulas are a tool for constructing multivariate distributions and formalizing the dependence structure between random variables. From copula literature review, there are a few asymmetric copulas available so far while data collected from the real world often exhibit asymmetric nature. This necessitates developing asymmetric copulas. In this study, we discuss a method to construct a new class of bivariate asymmetric copulas based on products of symmetric (sometimes asymmetric) copulas with powered arguments in order to determine if the proposed construction can offer an added value for modeling asymmetric bivariate data. With these newly constructed copulas, we investigate dependence properties and measure of association between random variables. In addition, the test of symmetry of data and the estimation of hyper-parameters by the maximum likelihood method are discussed. With two real example such as car rental data and economic indicators data, we perform the goodness-of-fit test of our proposed asymmetric copulas. For these data, some of the proposed models turned out to be successful whereas the existing copulas were mostly unsuccessful. The method of presented here can be useful in fields such as finance, climate and social science.

• Journal of Astronomy and Space Sciences
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• v.29 no.2
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• pp.97-101
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• 2012

Monitoring sunspots consistently is the most basic step required to study various aspects of solar activity. To achieve this goal, the observers must regularly calculate their own correction factor $k$ and keep it stable. Relatively recently, two observing teams in South Korea have presented interesting papers which claim that revisions that take the yearly-basis $k$ into account lead to a better agreement with the international relative sunspot number $R_i$, and that yearly $k$ apparently varies with the solar cycle. In this paper, using artificial data sets we have modeled the sunspot numbers as a superposition of random noise and a slowly varying background function, and attempted to investigate whether the variation in the correction factor is coupled with the solar cycle. Regardless of the statistical distributions of the random noise, we have found the correction factor increases as sunspot numbers increase, as claimed in the reports mentioned above. The degree of dependence of correction factor $k$ on the sunspot number is subject to the signal-to-noise ratio. Therefore, we conclude that apparent dependence of the value of the correction factor $k$ on the phase of the solar cycle is not due to a physical property, but a statistical property of the data.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Journal of Environmental Health Sciences
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• v.46 no.1
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• pp.65-77
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• 2020

Objectives: This study investigated the average number of drinkers in Korea, the number of high-risk drinkers, the average amount of alcohol consumed by high-risk drinkers, and the types of alcohol consumed according to the characteristics of the group of dependent drinkers. Methods: The results were obtained by analyzing the following data: The Global Status Report on Alcohol and Health; Country Profile 2014; WHO Country Profile 2014; Korea National Health and Nutrition Examination Survey 2014, Korean Statistical Information Service; National Tax Statistics-Liquor Tax; Gallup Drinking Frequency Survey 2015 Results: This study found that a large proportion of drinkers in Korea are already high-risk drinkers, and even among drinkers, alcohol consumption was highly biased. It was reported that 49.8% of men in the problem, abuse, and dependence groups accounted for 92.4% of total alcohol consumption among the male population. Notably, the 9.6% of men making up the dependent group consumed more than 30% of the alcohol ingested among males. Women had significant variations within groups that were considered high-risk and exhibited a large share of alcohol consumption in the problem (10.0% of the female population), abuse (1.8% of the female population), and dependence (1.5% of the female population) groups, constituting 72.8% of total alcohol consumption. The average amount of alcohol consumed by drinkers in Korea seems to have exceeded the level of intake by high-risk groups. Alcohol-dependent groups consumed 900.7 mL of soju, 405.2 mL of table wine, and 2,043.8 mL of beer, which is very similar to the consumption average of 2,031 mL of beer and 895.2 mL of soju in the drinking group. Conclusion: It has been shown that men's dependence on alcohol is serious, and it is possible to infer that alcohol consumption in some vulnerable groups is very high. As the average alcohol intake among alcohol-dependent groups and ordinary drinkers is very similar, it is highly likely that the drinker is an alcohol-dependent consumer in Korea.

• Korean Journal of Biological Psychiatry
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• v.24 no.4
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• pp.219-224
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• 2017

Objectives This study sought to investigate the relationship between age, sex and alterations in levels of % carbohydrate-deficient transferrin (%CDT) and gamma-glutamyl transferase (GGT) in patients admitted with alcohol dependence. Methods The study retrospectively enrolled 187 patients who were diagnosed with alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders-Fourth edition (DSM-IV) and were admitted into a closed ward in Hallym University Hangang Sacred Heart Hospital from 2009 to 2012 and Hallym University Kangnam Sacred Heart Hospital from 2012 to 2017. Demographic factors (age, sex) and biochemical markers [%CDT, GGT, mean corpuscular volume (MCV), aspartate transferase (AST), alanine transferase (ALT)] were collected by reviewing medical records. Alterations in the levels of %CDT and GGT in different groups for each demographic factor were compared after correcting for confounding variables (age, initial %CDT, GGT, MCV, AST, ALT). Results Decreased %CDT and GGT were observed during the period of abstinence after admission. The normalization period for %CDT increased with age, while the normalization period for GGT was longer in female patients. Conclusions These results suggest that alcohol-dependent patients that vary in age have different alterations in %CDT, while different sexes have different alterations in GGT. Age and sex can be potential indicators of treatment response after abstinence in patients with alcohol dependence. Further studies are needed to evaluate the relationship between these factors with regards to physiological and hematological changes in alcohol dependence.

• Journal of the Korean Institute of Gas
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• v.10 no.2 s.31
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• pp.7-13
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• 2006

In order to evaluate the fire and explosion involved and to ensure the safe and optimized operation of chemical processes, it is necessary to know combustion characteristics. The explosion limit, the heat of combustion, flame temperature and temperature dependence of the lower explosive limit are the major combustion characteristics used to determine the fire and explosion hazards of the flammable substances. The aim of this study is to investigate interrelationship of explosion characteristics and the temperature dependence of the lower explosion limit at elevated temperature for akylketones. By using the reference data, the empirical equations which describe the interrelationships of explosion properties of akylketones have been derived. Also, the new equations using the mathematical and statistical methods for predicting the temperature dependence of lower explosion limits of akylketones on the basis of the literature data are proposed. The values calculated by the proposed equations agreed with literature data within a few percent. From the given results, using the proposed methodology, it is possible to predict the explosion limits of the other flammable substances.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.215-231
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• 2017

The US presidential primaries take place sequentially in different places with a time lag. However, they have not attracted as much attention in terms of modelling as the US presidential election has. This study applied several autologistic models to find the relation between the outcome of the primary election for a Democrat candidate with socioeconomic attributes in consideration of spatial and temporal dependence. According to the result applied to the 2016 election data at the county level, Hillary Clinton was supported by people in counties with high population rates of old age, Black, female and Hispanic. In addition, spatial dependence was observed, representing that people were likely to support the same candidate who was supported from neighboring counties. Positive auto-correlation was also observed in the time-series of the election outcome. Among several autologistic models of this study, the model specifying the effect of Super Tuesday had the best fit.

• Tuberculosis and Respiratory Diseases
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• v.85 no.4
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• pp.349-357
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• 2022

Background: The most consistently identified mortality determinants for the new coronavirus 2019 (COVID-19) infection are aging, male sex, cardiovascular/respiratory diseases, and cancer. They were determined from heterogeneous cohorts that included patients with different disease severity and previous conditions. The main goal of this study was to determine if activities of daily living (ADL) dependence measured by Barthel's index could be a predictor for COVID-19 mortality. Methods: A prospective cohort study was performed with a consecutive sample of 340 COVID-19 patients representing patients from all over the northern region of Portugal from October 2020 to March 2021. Mortality risk factors were determined after controlling for demographics, ADL dependence, admission time, comorbidities, clinical manifestations, and delay-time for diagnosis. Central tendency measures were used to analyze continuous variables and absolute numbers (proportions) for categorical variables. For univariable analysis, we used t test, chi-square test, or Fisher exact test as appropriate (α=0.05). Multivariable analysis was performed using logistic regression. IBM SPSS version 27 statistical software was used for data analysis. Results: The cohort included 340 patients (55.3% females) with a mean age of 80.6±11.0 years. The mortality rate was 19.7%. Univariate analysis revealed that aging, ADL dependence, pneumonia, and dementia were associated with mortality and that dyslipidemia and obesity were associated with survival. In multivariable analysis, dyslipidemia (odds ratio [OR], 0.35; 95% confidence interval [CI], 0.17-0.71) was independently associated with survival. Age ≥86 years (pooled OR, 2.239; 95% CI, 1.100-4.559), pneumonia (pooled OR, 3.00; 95% CI, 1.362-6.606), and ADL dependence (pooled OR, 6.296; 95% CI, 1.795-22.088) were significantly related to mortality (receiver operating characteristic area under the curve, 82.1%; p<0.001). Conclusion: ADL dependence, aging, and pneumonia are three main predictors for COVID-19 mortality in an elderly population.

• 한국해양학회지
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• v.27 no.1
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• pp.35-45
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• 1992

This paper is concerned with the development and application of a stochastic model for predicting E1 nino phenomenon. For this, first a general criterion for determining E1 nino phenomenon, including period and strength, which is based on partial sum of monthly sea surface temperatures (SST) anomalies, is proposed, Secondly, the annual fluctuations, periodicity and dependence of monthly mean of equatorial Pacific SST during the period 1951-1990 are analyzed. Based on these, time series nonlinear regression model for the prediction of SST have been derived. A statistical procedure for using the model to predict the SST have been derived. A statistical procedure for using the model to predict the SST level is also proposed.