• Preventive Nutrition and Food Science
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• v.13 no.2
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• pp.104-111
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• 2008

As a way to account for the variability of the primary model parameters in the secondary modeling of microbial growth, three different regression approaches were compared in determining the confidence interval of the temperature-dependent primary model parameters and the estimated microbial growth during storage: bootstrapped regression with all the individual primary model parameter values; bootstrapped regression with average values at each temperature; and simple regression with regression lines of 2.5% and 97.5% percentile values. Temperature dependences of converted parameters (log $q_o$, ${\mu}_{max}^{1/2}$, log $N_{max}$) of hypothetical initial physiological state, maximum specific growth rate, and maximum cell density in Baranyi's model were subjected to the regression by quadratic, linear, and linear function, respectively. With an advantage of extracting the primary model parameters instantaneously at any temperature by using mathematical functions, regression lines of 2.5% and 97.5% percentile values were capable of accounting for variation in experimental data of microbial growth under constant and fluctuating temperature conditions.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.193-200
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• 2014

It was recognized by some researchers that the disturbance variance in a censored regression model is frequently underestimated by the maximum likelihood method. This underestimation has implications for the estimation of marginal effects and asymptotic standard errors. For instance, the actual coverage probability of the confidence interval based on a maximum likelihood estimate can be significantly smaller than the nominal confidence level; consequently, a Bayesian estimation is considered to overcome this difficulty. The behaviors of the maximum likelihood and Bayesian estimators of disturbance variance are examined in a fixed effects panel regression model with a limited dependent variable, which is known to have the incidental parameter problem. Behavior under random effect assumption is also investigated.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.251-261
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• 2004

The paper focuses primarily on the standard linear multiple regression model where the parameter of interest is a ratio of two regression coefficients. The general model includes the calibration model, the Fieller-Creasy problem, slope-ratio assays, parallel-line assays, and bioequivalence. We provide an orthogonal transformation (cf. Cox and Reid (1987)) of the original parameter vector. Also, we give some remarks on the difficulties associated with likelihood based confidence interval.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.329-339
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• 1993

Kernel regression is a nonparametric regression technique which requires only differentiability of the true function. If one wants to use the kernel regression technique to produce smooth estimates of a curve over a finite interval, one can realize that there exist distinct boundary problems that detract from the global performance of the estimator. This paper develops a kernel to handle boundary problem. In order to develop the boundary kernel, a generalized jacknife method by Gray and Schucany (1972) is adapted. Also, it will be shown that the boundary kernel has the same order of convergence rate as non-boundary.

• Food Science and Biotechnology
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• v.17 no.6
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• pp.1352-1356
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• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.627-641
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• 2021

An asymmetric least squares estimation method has been employed to estimate linear models for percentile regression. An asymmetric maximum likelihood estimation (AMLE) has been developed for the estimation of Poisson percentile linear models. In this study, we propose generalized nonlinear percentile regression using the AMLE, and the use of the parametric bootstrap method to obtain confidence intervals for the estimates of parameters of interest and smoothing functions of estimates. We consider three conditional distributions of response variables given covariates such as normal, exponential, and Poisson for three mean functions with one linear and two nonlinear models in the simulation studies. The proposed method provides reasonable estimates and confidence interval estimates of parameters, and comparable Monte Carlo asymptotic performance along with the sample size and quantiles. We illustrate applications of the proposed method using real-life data from chemical and radiation epidemiological studies.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.279-286
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• 2016

Recently, with the population growth of elderly people, concerns about oral health related to the quality of life of the elderly are increasing. The purpose of this study is to assess the association between the elderly women divided into dementia, suspected dementia, healthy groups and the remaining teeth of them. Total 177 elderly women of over 60 years old, visiting on senior center in some community dwelling, were assessed for oral condition and their cognitive function with MMSE score. All the collected data were analyzed by chi-square test, t-test, and multiful logistic regression using SPSS. Multiful logistic regression was used to analyze the relationship of dementia according to MMSE score and remaining teeth, and 95% confidence intervals were computed. Odds ratio(OR) of the number of remaining teeth 0-10 was 3.43 (95% confidence interval: 1.382-8.997). This study showed significant difference and the relationship between dementia according to MMSE score and the number of remaining teeth of the elderly women.

• The Journal of the Acoustical Society of Korea
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• v.31 no.1
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• pp.51-61
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• 2012

In this research, we have analyzed the trend of radiation pattern variation in relation to the change of design variables such as source interval and source number for conformal array transducers arranged in equi-angle, equi-interval, and geodesic dome forms. Through statistical multiple regression analysis of the results, we derived functional forms of the side lobe level and the beamwidth in terms of the design variables. Futhermore, the structure of the array transducer was optimized to achieve the smallest side lobe level while satisfying the requirements on beam width by the GA (genetic algorithm) method. Based on the optimized results, we have determined the equi-interval form as the optimal array geometry among the three conformal array geometries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.148-156
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• 2019

This study identified the effects of Depression, Anxiety, and Quality of Sleep on Excessive Daytime Sleepiness (EDS) in nursing students. Totally, 213 students of a nursing college located in C do, Korea, were enrolled for the study, which was accomplished through a questionnaire. Data were collected from May 20 to 30, 2019. The collected data were compiled using the SPSS/WIN 25.0 statistic program by applying Chi-square test, Paired t-test, Pearson's Coefficient, and Multiple logistic regression. The following results were obtained. The mean score for EDS was 8.95(±4.56), and Prevalence of EDS was determined to be 40.8%. The mean scores obtained for the affecting factors were Depression 10.05(±7.85), STAI-S 46.09(±9.50), STAI-T 46.47(±9.93), and Quality of Sleep 6.51(±2.95). Depression, STAI-S, STAI-T, and Quality of Sleep were significantly different for EDS and Non-EDS students (t=1.955, p=0.024; t=5.446, p<0.001; t=1.716, p=0.007; t=12.168, p<0.001; respectively). Multiple logistic regression revealed that factors associated with EDS were STAI-S and Quality of Sleep (adjusted odds ratio=1.04, 95% Confidence Interval=1.01-1.08; adjusted odds ratio=1.16, 95% Confidence Interval=1.04-1.29; respectively). These findings indicate the necessity to improve the quality of sleep and manage anxiety alleviation of nursing students, to reduce excessive daytime sleepiness.

• Journal of Industrial Convergence
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• v.18 no.6
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• pp.75-83
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• 2020

This study was a descriptive exploratory study to provide a foundation for improving the healthy working environment in Korean Red Cross Blood Center workers. A total of 215 surveys were collected from September 8 to October 31, 2020. Chi-squared test or Fisher's extract test, Independent t-tests, and Multiple Logistic Regression were performed with the SPSS 19.0 statistical program. We conducted multiple logistic regression analysis to evaluate the relationship between positive psychology capital and job stress with the perceived health status. The good perceived health status was 35.3%(N=76). When adjusted for related factors, the general characteristics and work characteristics were not related to perceived health status, and the positive psychological capital and job stress were significantly different between healthy and unhealthy groups (p<.001, p<.001). As positive psychology capital increased by one unit, the odds ratio of good health groups for subjective health increased significantly 1.1 times [95% Confidence interval (CI: 1.0-1.1)] and the odds ratio for good health groups for subjective health status decreased by 0.9 times [95% Confidence interval (CI: 0.8-0.9] as job stress increased by one unit.