• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.115-127
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• 2017

This paper proposes a Bayesian test for the equivalence of survival functions in multiple groups. Proposed Bayesian test use the model of Cox's regression with time-varying coefficients. B-spline expansions are used for the time-varying coefficients, and the proposed test use only the partial likelihood, which provides easier computations. Various simulations of the proposed test and typical tests such as log-rank and Fleming and Harrington tests were conducted. This result shows that the proposed test is consistent as data size increase. Specifically, the power of the proposed test is high despite the existence of crossing hazards. The proposed test is based on a Bayesian approach, which is more flexible when used in multiple tests. The proposed test can therefore perform various tests simultaneously. Real data analysis of Larynx Cancer Data was conducted to assess applicability.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.69-78
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• 2019

A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

• Journal of Nutrition and Health
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• v.52 no.6
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• pp.552-558
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• 2019

Purpose: Although vitamin D deficiency is common among Korean adolescent girls and young women, few studies have explored the potential health effects of vitamin D deficiency in this vulnerable population. This study examined the association between vitamin D deficiency and anemia in Korean adolescent girls and young women. Methods: The data from the Korea National Health and Nutrition Examination Survey 2008 ~ 2014 were used. A total of 3,643 girls and adult women aged 12 to 29 who provided all the information (including serum 25-hydroxy vitamin D, hemoglobin, and/or serum ferritin) needed for the analysis were included in the analysis. Demographic, lifestyle, and health data were obtained through survey questionnaires. Anemia and iron deficiency anemia were defined according to the World Health Organization cut-offs. Multivariable logistic regression, and restricted cubic spline regression were used in the analysis. Results: In fully adjusted logistic regression models, the vitamin D deficiency was significantly associated with higher prevalences of anemia (odds ratio (OR): 1.61, 95% confidence interval (CI): 1.04 ~ 2.49) and iron deficiency anemia (OR: 1.43, 95% CI: 1.01 ~ 2.03). In a cubic spline regression model, we observed a dose-response relationship between serum 25(OH)D concentration and anemia, and this linear relationship was also clearly observed between serum 25(OH)D concentration and iron deficiency anemia. Conclusion: Vitamin D deficiency may be associated with a higher prevalence of iron deficiency anemia and anemia in adolescent girls and young women. Alternatively, vitamin D deficiency may be a concurrent event for patients with anemia, which we cannot distinguish in this cross-sectional study. Further studies are needed to verify the causality in this population of low vitamin D levels.

• Geomechanics and Engineering
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• v.21 no.6
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• pp.583-598
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• 2020

This study is attempted to propose a new hybrid artificial intelligence model called integrative genetic algorithm with multivariate adaptive regression splines (GA-MARS) for settlement prediction of shallow foundations on sandy soils. In this hybrid model, the evolution algorithm - Genetic Algorithm (GA) was used to search and optimize the hyperparameters of multivariate adaptive regression splines (MARS). For this purpose, a total of 180 experimental data were collected and analyzed from available researches with five-input variables including the bread of foundation (B), length to width (L/B), embedment ratio (D_f/B), foundation net applied pressure (q_net), and average SPT blow count (N_SPT). In further analysis, a new explicit formulation was derived from MARS and its accuracy was compared with four available formulae. The attained results indicated that the proposed GA-MARS model exhibited a more robust and better performance than the available methods.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.709-720
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• 2017

In healthcare and medical research, many important variables have a measurement error such as body mass index and laboratory data. It is also not easy to collect samples of large size because of high cost and long time required to collect the target patient satisfied with inclusion and exclusion criteria. Beside, the demand for solving a complex scientific problem has highly increased so that a semiparametric regression approach could be of substantial value solving this problem. To address the issues of measurement error, small domain and a scientific complexity, we conduct a multivariable Bayesian smoothing under structural measurement error covariate in this article. Specifically we enhance our previous model by incorporating other useful auxiliary covariates free of measurement error. For the regression spline, we use a radial basis functions with fixed knots for the measurement error covariate. We organize a fully Bayesian approach to fit the model and estimate parameters using Markov chain Monte Carlo. Simulation results represent that the method performs well. We illustrate the results using a national survey data for application.

• Computers and Concrete
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• v.25 no.1
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• pp.1-14
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• 2020

This study presents applications of the multivariate adaptive regression splines (MARS) method for predicting the ultimate loading carrying capacity (N_u) of rectangular concrete-filled steel tubular (CFST) columns subjected to eccentric loading. A database containing 141 experimental data was collected from available literature to develop the MARS model with a total of seven variables that covered various geometrical and material properties including the width of rectangular steel tube (B), the depth of rectangular steel tube (H), the wall thickness of steel tube (t), the length of column (L), cylinder compressive strength of concrete (f'_c), yield strength of steel (f_y), and the load eccentricity (e). The proposed model is a combination of the MARS algorithm and the grid search cross-validation technique (abbreviated here as GS-MARS) in order to determine MARS' parameters. A new explicit formulation was derived from MARS for the mentioned input variables. The GS-MARS estimation accuracy was compared with four available mathematical methods presented in the current design codes, including AISC, ACI-318, AS, and Eurocode 4. The results in terms of criteria indices indicated that the MARS model was much better than the available formulae.

• Nutrition Research and Practice
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• v.15 no.4
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• pp.468-478
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• 2021

BACKGROUND/OBJECTIVES: This study aimed to analyze the association between dietary omega-3 fatty acid intake and depression in postmenopausal women using data from the Korea National Health and Nutrition Examination Survey (KNHANES) VI. SUBJECTS/METHODS: The KNHANES is a cross-sectional nationwide health and nutrition survey. Dietary data, including omega-3 fatty acids, were assessed using the 24-h recall method. Depression was evaluated using a survey questionnaire. The association between dietary omega-3 fatty acids and depression was evaluated using multivariate logistic regression analysis. Depression, according to the dietary omega-3 fatty acid intake, was expressed as the odds ratio (OR) with a 95% confidence interval (CI). A total of 4,150 postmenopausal women were included in the analysis. RESULTS: In the fully-adjusted model, the group with the highest dietary omega-3 fatty acid intake significantly showed lower prevalence of depression than the group with the lowest intake (OR, 0.52; 95% CI, 0.33-0.83); a significant linear trend was detected (P for trend = 0.04). According to the dose-response analysis using cubic restricted spline regression, this association was linear and monotonic (P for non-linearity = 0.32). CONCLUSIONS: In this study, the dietary omega-3 fatty acid intake in postmenopausal women was inversely proportional to depression in a dose-response manner. Large cohort studies are needed to verify the causality between omega-3 fatty acids and depression in Korean postmenopausal women.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.513-521
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• 2020

In this paper, we have developed a Fama - French five factor model (FF5 model) from Fama & French (2015) by using concept of time-varying coefficient. For a data set, we have used monthly data form Kenneth R. French home page, it include Japan portfolios (classified by using size and book-to-market) and 5 factors from July 1990 to April 2020. The first analysis, we used Augmented Dickey-Fuller test (ADF test) for the stationary test, from the result, all Japan portfolios and 5 factors are stationary. Next analysis, we estimated a coefficient of Fama - French five factor model by using a generalized additive model with a thin-plate spline to create the time-varying coefficient Fama - French five factor model (TV-FF5 model). The benefit of this study is TV-FF5 model which can capture a different effect at different times of 5 factors but the traditional FF5 model can't do it. From the result, we can show a time-varying coefficient in all factors and in all portfolios, for time-varying coefficients of Rm-Rf, SMB, and HML are significant for all Japan portfolios, time-varying coefficients of RMW are positively significant for SM, and SH portfolio and time-varying coefficients of CMA are significant for SM, SH, and BM portfolio.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.713-722
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• 2009

The estimation of odds ratio and corresponding confidence intervals for case-control data have been done by traditional generalized linear models which assumed that the logarithm of odds ratio is linearly related to risk factors. We adapt a lower-dimensional approximation of Gu and Kim (2002) to provide a faster computation in nonparametric method for the estimation of odds ratio by allowing flexibility of the estimating function and its Bayesian confidence interval under the Bayes model for the lower-dimensional approximations. Simulation studies showed that taking larger samples with the lower-dimensional approximations help to improve the smoothing spline estimates of odds ratio in this settings. The proposed method can be used to analyze case-control data in medical studies.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.57-76
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• 2007

Kriging is a nonparametric regression method used in geostatistics for estimating curves and surfaces for spatial data. It may come as a surprise that the Kriging estimator, normally derived as the best linear unbiased estimator, is also the solution of a particular variational problem. Thus, Kriging estimators can also be interpreted as generalized smoothing splines where the roughness penalty is determined by the covariance function of a spatial process. We build off the early work by Silverman (1982, 1984) and the analysis by Cox (1983, 1984), Messer (1991), Messer and Goldstein (1993) and others and develop an equivalent kernel interpretation of geostatistical estimators. Given this connection we show how a given covariance function influences the bias and variance of the Kriging estimate as well as the mean squared prediction error. Some specific asymptotic results are given in one dimension for Matern covariances that have as their limit cubic smoothing splines.