• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.575-585
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• 2016

Longitudinal studies repeatedly measure outcomes over time. Therefore, repeated measurements are serially correlated from same subject (within-subject variation) and there is also variation between subjects (between-subject variation). The serial correlation and the between-subject variation must be taken into account to make proper inference on covariate effects (Diggle et al., 2002). However, estimation of the covariance matrix is challenging because of many parameters and positive definiteness of the matrix. To overcome these limitations, we propose autoregressive moving average Cholesky decomposition (ARMACD) for the linear mixed models. The ARMACD allows a class of flexible, nonstationary, and heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the random effects covariance matrix. We analyze a real dataset to illustrate our proposed methods.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.435-441
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• 2021

Objective: To evaluate the usefulness of the ventricular volume percentage quantified using three-dimensional (3D) brain computed tomography (CT) data for interpreting serial changes in hydrocephalus. Materials and Methods: Intracranial and ventricular volumes were quantified using the semiautomatic 3D threshold-based segmentation approach for 113 brain CT examinations (age at brain CT examination ≤ 18 years) in 38 patients with hydrocephalus. Changes in ventricular volume percentage were calculated using 75 serial brain CT pairs (time interval 173.6 ± 234.9 days) and compared with the conventional assessment of changes in hydrocephalus (increased, unchanged, or decreased). A cut-off value for the diagnosis of no change in hydrocephalus was calculated using receiver operating characteristic curve analysis. The reproducibility of the volumetric measurements was assessed using the intraclass correlation coefficient on a subset of 20 brain CT examinations. Results: Mean intracranial volume, ventricular volume, and ventricular volume percentage were 1284.6 ± 297.1 cm³, 249.0 ± 150.8 cm³, and 19.9 ± 12.8%, respectively. The volumetric measurements were highly reproducible (intraclass correlation coefficient = 1.0). Serial changes (0.8 ± 0.6%) in ventricular volume percentage in the unchanged group (n = 28) were significantly smaller than those in the increased and decreased groups (6.8 ± 4.3% and 5.6 ± 4.2%, respectively; p = 0.001 and p < 0.001, respectively; n = 11 and n = 36, respectively). The ventricular volume percentage was an excellent parameter for evaluating the degree of hydrocephalus (area under the receiver operating characteristic curve = 0.975; 95% confidence interval, 0.948-1.000; p < 0.001). With a cut-off value of 2.4%, the diagnosis of unchanged hydrocephalus could be made with 83.0% sensitivity and 100.0% specificity. Conclusion: The ventricular volume percentage quantified using 3D brain CT data is useful for interpreting serial changes in hydrocephalus.

• Journal of Korean Society of Transportation
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• v.17 no.3
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• pp.47-60
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• 1999

In this study, the dynamic route choice behavior of driver is assumed to be affected by the current conditions of traffic environments as well as the Past traffic environments and activities. The repeated survey of multi-timed to owner drove in Chon-ju city by the virtual traffic information system was performed by the stated preference method. And the LISREL(An analysis of linear structural relationship) model was used. As the results. the variable Parameter and t-value of travel time information on applied model was high and their results have an effect greatly to the route choice. After all, the route choice was negative from Kirin-ro, which Penetrates the downtown, and positive from Chunbyun-ro which was a roundabout way was confirmed. Estimated result of the reasonability, the highest suitable model was ode which applied the concept of serial correlation and stated dependence and was shown applying rightfulness to dynamic model. As the serial correlation and stated dependence parameter value, when time interval was large, parameter value was small and the serial correlation and stated dependence was in inverse proportion ratio to the time interval.

• The korean journal of orthodontics
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• v.10 no.1
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• pp.81-93
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• 1980

The interrelationships between growth rates (and size) of the selected cranifacial dimensions and body dimensions (height and weight) were investigated in the longitudinal data of primary school children from 6 to 11 years of age. The data were obtained from serial cephalometric radiographs and health record which were taken at one year interval. Regression analyses were used to analyze the data. The main concludions might be summarized as follows; 1. Size relationships between body height (and weigh) and S-Gn, posterior facial height (s-Go), total mandibular length (Ar-Gn) showed high significant correlation, but no association between body height, weight and anterior cranial base length (S-N). 2. Correlation coefficients between facial dimensions and body height (and weight) were getting lower with age increase. 3. At all age groups, significant prediction equation for some facial dimensions with body height and weight were obtained. 4. In this sample, the growth rates of facial dimensions and body height and weight showed almostly constant during this age period and the growth rate of body height and weight of girls was exceeded that of boys. 5. A relatively high degree of variation between individuals existed in the sample. 6. A positive correlation was found for the relationship between the growth rates of facial dimensions and those of body height (and weight) in boys and girls, but was not found in total samples.

• The Journal of Korean Physical Therapy
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• v.22 no.2
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• pp.7-13
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• 2010

Purpose: This study was designed to determine the correlation between faster response time and functional activities of brain regions during cognitive time management. Methods: Twelve healthy subjects participated in this experiment. Subjects performed the serial reaction time task (SRTT), which was designed by the Superlab program, during fMRI scanning. When the 'asterisk' appeared in the 4 partition spaces on the monitor, the subject had to press the correct response button as soon as possible. Results: fMRI results showed activation of the left primary sensorimotor cortex, both premotor areas, the supplementary motor area, posterior parietal cortex and cerebellum. There were significant correlations, from moderate to strong, between faster reaction time and BOLD signal intensity in activated areas. Conclusion: These results suggest that motor skill learning to be needed cognitive time management is associated with greater activation of large scale sensorimotor networks.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.311-324
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• 2021

Investors have increasingly become interested in macroeconomic antecedents in order to better understand the investment environment and estimate the scope of profitable investment in equity markets. This study endeavors to examine the interdependency between the macroeconomic antecedents (international oil price (COP), Domestic gold price (GP), Rupee-dollar exchange rates (ER), Real interest rates (RIR), consumer price indices (CPI)), and the BSE Sensex and Nifty 50 index return. The data is converted into a natural logarithm for keeping it normal as well as for reducing the problem of heteroscedasticity. Monthly time series data from January 1992 to July 2019 is extracted from the Reserve Bank of India database with the application of financial Econometrics. Breusch-Godfrey serial correlation LM test for removal of autocorrelation, Breusch-Pagan-Godfrey test for removal of heteroscedasticity, Cointegration test and VECM test for testing cointegration between macroeconomic factors and market returns,] are employed to fit regression model. The Indian market returns are stable and positive but show intense volatility. When the series is stationary after the first difference, heteroskedasticity and serial correlation are not present. Different forecast accuracy measures point out macroeconomics can forecast future market returns of the Indian stock market. The step-by-step econometric tests show the long-run affiliation among macroeconomic antecedents.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.8 no.11
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• pp.27-34
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• 1985

Recently the distributed lag models for time series data have been used in several quantitative analyses. But the analyses of time series which have the serial correlations in error terms and the lagged values of dependent variables violate the hypothesis of OLS method. This paper suggests that the approach technique of distributed lay model with serial correlation should be applied by the Bayesian inference to estimate the parameters. For the application of distributed lag model by Bayesian analysis, the data for monthly consumption expenditure per household by items of commodities from 1972 to 1981 are used in order to estimate the lagged coefficient of processed food and the regression coefficient of the food and beverage.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.649-655
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• 2009

Mean acquisition time (MAT) is the most important performance measure for code acquisition systems, where a shorter MAT implies a better code acquisition performance. Keshavarzian and Salehi proposed the multiple-shift (MS) algorithm for code acquisition in optical code division multiple access (O-CDMA) systems. Performing two steps acquisition, the MS algorithm has a shorter MAT than that of the conventional serial-search (SS) algorithm. In this paper, we propose a rapid code acquisition algorithm for O-CDMA systems. By using an efficient combination of local signals, correlation value, and the sign of correlation value, the proposed algorithm can provide a shorter MAT compared with that of the MS algorithm. The simulation results show that the proposed algorithm presents a shorter MAT than that of the MS algorithm.

• Archives of Plastic Surgery
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• v.44 no.6
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• pp.523-529
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• 2017

Background Serial tissue expansion is performed to remove giant congenital melanocytic nevi. However, there have been no studies comparing the expansion rate between the subsequent and preceding expansions. In this study, we analyzed the rate of expansion in accordance with the number of surgeries, expander location, expander size, and sex. Methods A retrospective analysis was performed in pediatric patients who underwent tissue expansion for giant congenital melanocytic nevi. We tested four factors that may influence the expansion rate: The number of surgeries, expander location, expander size, and sex. The rate of expansion was calculated by dividing the 'inflation amount' by the 'expander size'. Results The expansion rate, compared with the first-time group, was 1.25 times higher in the second-or-more group (P=0.04) and 1.84 times higher in the third-or-more group (P<0.01). The expansion rate was higher at the trunk than at other sites (P<0.01). There was a tendency of lower expansion rate for larger expanders (P=0.03). Sex did not affect the expansion rate. Conclusions There was a positive correlation between the number of surgeries and the expansion rate, a positive correlation between the expander location and the expansion rate, and a negative correlation between the expander size and the expansion rate.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.181-189
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• 1993

Over the past two decades, forecasting has gained widespread acceptance as an integral part of business planning and decision making. Accurate forecasting is a prerequisite to successful planning. Accordingly, recent advances in forecasting techniques are of exceptional value to corporate planners. But most of forecasting mothods are reveal its limit and problem for precision and reliability duing to each relationship for raw data and possibility of explanation for each variable. Therefore, to construct the Integrated Forecasting Model(IFM) for Information Factor Analysis, it shoud be considered that whether law data has time lag and variables are explained. For this. following several method can be used : Least Square Method, Markov Process, Fibonacci series, Auto-Correlation, Cross-Correlation, Serial Correlation and Random Walk Theory. Thus, the unified property of these several functions scales the safety and growth of the system which may be varied time-to-time.