• The Journal of Korean Physical Therapy
• /
• v.23 no.3
• /
• pp.65-70
• /
• 2011

Purpose: We investigated the changes in the stop-signal reaction time (SSRT) and the no-signal reaction time (NSRT) following motor sequential learning in the stop-signal task (SST). This study also determined which of the reduction0s of spatial processing time was better between blocked- and random-SST. Methods: Thirty right-handed healthy subjects without a history of neurological dysfunction were recruited. In all subjects, both the SSRT and the NSRT were measured for the SST. Tasks were classified into two categories based on the stop-signal patterns, the blocked-SST practice group and random-SST practice group. All subjects gave written informed consent. Results: In the blocked-SST group, both the SSRT and the NSRT was significantly decreased (p<0.05) but not significantly changed in the random-SST group. In the SSRT and the NSRT, the blocked-SST group was faster than the random-SST group (p<0.05). In the post-test SST after practice of each group, the SSRT was significantly decreased in the random-SST group (p<0.05), but the NSRT showed no significant changes in either group. Conclusion: These findings demonstrate that random-SST practice resulted in a decrease in internal processing times needed for a rapid stop to visual signals, indicating motor skill learning is acquired through improved response selection and inhibition.

• The Journal of Korean Physical Therapy
• /
• v.27 no.4
• /
• pp.240-245
• /
• 2015

Purpose: The purpose of this study was to compare the blocked practice and random practice of task-oriented training in patients with chronic stroke to determine the effect of lower extremity muscle activity and balance ability. Methods: The thirty participants were randomly assigned to either the block practice group (BP) group or the random practice group (RP) and received the training three times per week, 30 minutes per day, for six weeks. Surface electromyography was used for measurement of lower extremity muscle activity. Static balance was to measured the stability index (SI) and weight distribution index (WDI) using the Tetrax. The four square step test (FSST) was used to measure dynamic balance. The paired t-test was used for determination of differences before and after intervention, and the independent t-test was used for determination of differences between groups. Results: Lower extremity muscle activity, RA and GCM was improved in the RP group after intervention and between groups. TA was significantly improved in the RP group compared with the BP group. In comparison of before and after interventions, SI was reduced in BP and RP. WDI in OS was reduced in comparison of BP and RP before and after intervention. CS was reduced in BP and RP. The OS and CS was improved in RP compared with BP. In comparison of before and after intervention, FSST was improved in BP and RP. Conclusion: Task-oriented training methods using random practice was found to be effective in promoting lower extremity muscle activity and balance ability in chronic stroke patients.

• Journal of Korean Society of Forest Science
• /
• v.104 no.1
• /
• pp.111-116
• /
• 2015

The purpose of this study was to evaluate mixed model of dbh-height relation containing random effect. Data were obtained from a survey site for Quercus variabilis in Gangwon region and remeasured the same site after three years. The mixed model were used to fixed effect in the dbh-height relation for Quercus variabilis, with random effect representing correlation of survey period were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -0.0291, 0.1007, respectively. The model with random effect (AIC = -215.5) has low AIC value, comparison with model with fixed effect (AIC = -154.4). It is for this reason that random effect associated with categorical data is used in the data fitting process, the model can be calibrated to fit repeated site by obtaining measurements. Therefore, the results of this study could be useful method for developing model using repeated measurement.

• Journal of applied mathematics & informatics
• /
• v.24 no.1_2
• /
• pp.221-230
• /
• 2007

The exponential and the gamma distributions have been the traditional models for drought duration and drought intensity data, respectively. However, it is often assumed that the drought duration and drought intensity are independent, which is not true in practice. In this paper, an application of the bivariate gamma exponential distribution is provided to drought data from Nebraska. The exact distributions of R=X+Y, P=XY and W=X/(X+Y) and the corresponding moment properties are derived when X and Y follow this bivariate distribution.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.4
• /
• pp.1429-1440
• /
• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• Nuclear Engineering and Technology
• /
• v.54 no.6
• /
• pp.2084-2093
• /
• 2022

Probabilistic safety assessment is widely used to quantify the risks of nuclear power plants and their uncertainties. When the lognormal distribution describes the uncertainties of basic events, the uncertainty of the top event in a fault tree is approximated with the sum of lognormal random variables after minimal cutsets are obtained, and rare-event approximation is applied. As handling complicated analytic expressions for the sum of lognormal random variables is challenging, several approximation methods, especially Monte Carlo simulation, are widely used in practice for uncertainty analysis. In this study, a theoretical approach for analyzing the sum of lognormal random variables using an efficient numerical integration method is proposed for uncertainty analysis in probability safety assessments. The change of variables from correlated random variables with a complicated region of integration to independent random variables with a unit hypercube region of integration is applied to obtain an efficient numerical integration. The theoretical advantages of the proposed method over other approximation methods are shown through a benchmark problem. The proposed method provides an accurate and efficient approach to calculate the uncertainty of the top event in probabilistic safety assessment when the uncertainties of basic events are described with lognormal random variables.

• Communications for Statistical Applications and Methods
• /
• v.26 no.3
• /
• pp.261-272
• /
• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• Research in Community and Public Health Nursing
• /
• v.20 no.4
• /
• pp.503-512
• /
• 2009

Purpose: This study was to investigate the factors influencing hotel workers' health practice. Methods: This study was based on the partial PRECEDE model. The subjects of this study were 261 servers sampled at random from a hotel located in Seoul. For the statistical analysis of collected data, descriptive statistics, t-test, ANOVA and logistic regression were performed with the SAS (Version. 8.01) program. Results: There were statistically significant primary factors influencing different parts of health practice. That is, regular exercise was influenced by gender, age and marital status, diet habit was by marital status, and type of working, prohibition of smoking was by gender, age and type of employment, and drinking by gender and job stress. Conclusion: This study has a limitation in generalized application to hotels in this country because it is a cross-sectional examination about the factors affecting health practice in the employees of a hotel. Further study is needed with various and broad variables that promote health practice and contributed to the development of health promotion programs.

• Communications for Statistical Applications and Methods
• /
• v.21 no.1
• /
• pp.45-60
• /
• 2014

We introduce a MCMC sampling for a generalized linear normal random effects model with the ordered probit link function based on latent variables from suitable truncated normal distribution. Such models have proven useful in practice and we have observed numerically reasonable results in the estimation of fixed effects when the random effect term is provided. Applications that utilize Korean Welfare Panel Study data can be difficult to model; subsequently, we find that an ordered probit model with the random effects leads to an improved analyses with more accurate and precise inferences.

• The Korean Journal of Applied Statistics
• /
• v.24 no.1
• /
• pp.177-183
• /
• 2011

Hidden Markov random eld(HMRF) is one of the most common model for image segmentation which is an important preprocessing in many imaging devices. The HMRF has unknown hyper-parameters on Markov random field to be estimated in segmenting testing images. However, in practice, due to computational complexity, it is often assumed to be a fixed constant. In this paper, we numerically show that the segmentation results very depending on the fixed hyper-parameter, and, if the parameter is misspecified, they further depend on the choice of the class-labelling algorithm. In contrast, the HMRF with estimated hyper-parameter provides consistent segmentation results regardless of the choice of class labelling and the estimation method. Thus, we recommend practitioners estimate the hyper-parameter even though it is computationally complex.