• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Journal of the Korean Data and Information Science Society
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• 제21권2호
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• pp.379-385
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• 2010

This paper considers Bayesian approach to modeling a flexible regression function under functional measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under functional measurement error model without semiparametric component.

• 근관절건강학회지
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• 제20권3호
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• pp.225-234
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• 2013

Purpose: This purpose of study was to investigate the relationships among functional state, self-efficacy, and life satisfaction in the elderly with decreased visual acuity. Methods: The subjects were 162 elderly people from the G university hospital. Functional state was measured by Late-Life Function and Disability Instrument (LLFDI) and Minimum Data Set-Home Care version 2.0 (MDS HC 2.0). Self-efficacy and Life satisfaction were measured by the tool of Rho & Lee (2011) and Yoon (2007). Data were analyzed using t-test, ANOVA, Pearson's Correlation Coefficient, and logistic regression. Results: The daily life function was significantly associated with self-efficacy and vision decrease. The regression model with these two variables explained 35.6% of the variance of daily life function. IADL was significantly associated with vision decrease, age, gender, and self-efficacy. The regression model with the three variables explained 52.9% of the variance of IADL. Life satisfaction is significantly associated with self-efficacy, daily life function, vision decrease and IADL. The last regression model with the four variables explained 51.8% of the variance of life satisfaction. Conclusion: The levels of functional state, self-efficacy and life satisfaction in the elderly with decreased visual acuity were low. Self-efficacy was an important factor that influences on the functional state and life satisfaction. Therefore, nursing interventions that can enhance the self-efficacy are required in order to increase the functional state and life satisfaction in the elderly with decreased visual acuity.

• Journal of the Korean Data and Information Science Society
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• 제26권3호
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• pp.749-754
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• 2015

This article presents Bayesian approach to regression splines with knots on a grid of equally spaced sample quantiles of the independent variables under functional measurement error model.We consider small area model by using penalized splines of non-linear pattern. Specifically, in a basis functions of the regression spline, we use radial basis functions. To fit the model and estimate parameters we suggest a hierarchical Bayesian framework using Markov Chain Monte Carlo methodology. Furthermore, we illustrate the method in an application data. We check the convergence by a potential scale reduction factor and we use the posterior predictive p-value and the mean logarithmic conditional predictive ordinate to compar models.

• 한국과학교육학회지
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• 제32권3호
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• pp.524-531
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• 2012

Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI. Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed. Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.

• PNF and Movement
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• 제18권3호
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• pp.305-313
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• 2020

Purpose: The aim of this study was to investigate the relationship between a functional evaluation model and the Fugl-Meyer assessment (FMA) scale in evaluating the upper extremities of stroke patients Methods: Thirty-eight stroke patients were evaluated using the FMA and performed reaching and grasping motions using a three-dimensional motion analysis (Qquas 1 series, Qualisys AB, Sweden). The participants sat on a chair with a backrest. The position of the cup was located at a distance of 80% to the front arm length. The markers were attached to the sternum, acromion, elbow lateral epicondyle, ulnar styloid process, three metacarpal heads, and the distal phalanges of the thumb and index finger. The variables of the correlation between the functional evaluation model and the FMA scale were analyzed. Multiple regression (stepwise) was used to investigate the effect of the kinematic variables. Results: A significant negative correlation was found between the movement time (p < 0.05), movement unit (p < 0.05), and trunk displacement values (p < 0.05) in the FMA total scores, while a positive correlation was found between the peak velocity (p < 0.05) and maximum grip aperture values (p < 0.05). As a result of the multiple regression analysis, the most significant factor was the movement unit, followed by the general movement assessment and trunk displacement. The explained FMA total score value was 62%. Conclusion: This study presents a new functional evaluation model for assessing the reaching and grasping ability of stroke patients. The factors of the proposed functional evaluation model showed significant correlations with the FMA scale scores and confirmed that the new functional evaluation model explained the FMA by 67%. This suggests a new functional evaluation model for reaching and grasping stroke patients.

• Journal of the Korean Data and Information Science Society
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• 제15권3호
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• pp.617-624
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• 2004

In this article the fuzzy number rank and the fuzzy rank transformation method are introduced in order to analyse the non-parametric fuzzy regression model which cannot be described as a specific functional form such as the crisp data and fuzzy data as a independent and dependent variables respectively. The effectiveness of fuzzy rank transformation methods is compared with other methods through the numerical examples.

• Communications for Statistical Applications and Methods
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• 제27권6호
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• pp.603-624
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• 2020

There is an emerging interest in brain functional connectivity (FC) based on functional Magnetic Resonance Imaging in Alzheimer's disease (AD) studies. The complex and high-dimensional structure of FC makes it challenging to explore the association between altered connectivity and AD susceptibility. We develop a pipeline to refine FC as proper covariates in a penalized logistic regression model and classify normal and AD susceptible groups. Three different quantification methods are proposed for FC refinement. One of the methods is dimension reduction based on common component analysis (CCA), which is employed to address the limitations of the other methods. We applied the proposed pipeline to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data and deduced pathogenic FC biomarkers associated with AD susceptibility. The refined FC biomarkers were related to brain regions for cognition, stimuli processing, and sensorimotor skills. We also demonstrated that a model using CCA performed better than others in terms of classification performance and goodness-of-fit.

• Environmental Engineering Research
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• 제19권3호
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• pp.247-253
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• 2014

The aim of this study was to characterize quintinite in fluoride removal from aqueous solutions, using batch experiments. Experimental results showed that the maximum adsorption capacity of fluoride to quintinite was 7.71 mg/g. The adsorption of fluoride to quintinite was not changed at pH 5-9, but decreased considerably in highly acidic (pH < 3) and alkaline (pH > 11) solution conditions. Kinetic model analysis showed that among the three models (pseudo-first-order, pseudo-second-order, and Elovich), the pseudo-second-order model was the most suitable for describing the kinetic data. From the nonlinear regression analysis, the pseudo-second-order parameter values were determined to be $q_e=0.18mg/g$ and $k_2=28.80g/mg/hr$. Equilibrium isotherm model analysis demonstrated that among the three models (Langmuir, Freundlich, and Redlich-Peterson), both the Freundlich and Redlich-Peterson models were suitable for describing the equilibrium data. The model analysis superimposed the Redlich-Peterson model fit on the Freundlich fit. The Freundlich model parameter values were determined from the nonlinear regression to be $K_F=0.20L/g$ and 1/n=0.51. This study demonstrated that quintinite could be used as an adsorbent for the removal of fluoride from aqueous solutions.

• 한국안전학회지
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• 제36권1호
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• pp.1-8
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• 2021

To simulate a process model in the field of chemical engineering, it is very important to identify the physical properties of novel materials as well as existing materials. However, it is difficult to measure the physical properties throughout a set of experiments due to the potential risk and cost. To address this, this study aims to develop a property prediction model based on the group contribution method for aromatic chemical compounds including benzene rings. The benzene rings of aromatic materials have a significant impact on their physical properties. To establish the prediction model, 42 important functional groups that determine the physical properties are considered, and the total numbers of functional groups on 147 aromatic chemical compounds are counted to prepare a dataset. Support vector regression is employed to prepare a prediction model to handle sparse and high-dimensional data. To verify the efficacy of this study, the results of this study are compared with those of previous studies. Despite the different datasets in the previous studies, the comparison indicated the enhanced performance in this study. Moreover, there are few reports on predicting the physical properties of aromatic compounds. This study can provide an effective method to estimate the physical properties of unknown chemical compounds and contribute toward reducing the experimental efforts for measuring physical properties.