• 한국응용과학기술학회지
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• 제41권2호
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• pp.318-328
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• 2024

본 연구에서는 수크로스 스테아레이트(SS)의 유화력과 가용화력을 좀 더 향상시키기 위해, sodium deoxycholate (SDOC)와 PEG-40 hydrogenated castor oil (HCO)와의 혼합 조성물을 구성하였다. 혼합물의 최적 조성을 구하기 위해 혼합물 실험 계획법을 도입하여 실험을 진행하고, 실험에서 얻은 데이터를 회귀 분석하여 혼합물 조성의 변화가 혼합물의 특성에 미치는 영향을 살펴보았다. SS에 SDOC만을 첨가했을 때 코코넛 오일에 대한 유화력이 가장 향상되고, HCO만을 첨가했을 때는 가용화력이 가장 향상된 반면, 에스테르 오일인 cetyl ethylhexanoate (CEH)에 대한 유화력은 SDOC와 HCO를 함께 첨가했을 때 가장 크게 향상됨을 알 수 있었다. 코코넛 오일 및 CEH 각각에 대한 유화력과 가용화력, 3개의 특성치에 대한 동시 최적화를 실시한 결과, 최적의 계면활성제 혼합 조성은 SS 0.7939, SDOC 0.0586, HCO 0.1475로 구해졌다.

• 동아시아식생활학회지
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• 제1권3호
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• pp.305-311
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• 1991

The study was designed to investigate overall acceptability of rice extrudate with added ginseng flour extruded by single screw extruder. Graphic three dimension analysis on response surface regression was conducted for overall acceptability evaluated by balanced incomplete block design. Overall acceptability, which formed a saddle point, increased as moisture content increased at lower die temperature, and as moisture content decreased at higher die temperature. Critical values of each variable which indicated optimum response are 5.0% ginseng content, 17.8% moisture content and 104.6$^{\circ}C$ die temperature, and optimum inferred score of overall acceptability is 59.6 and 90. Key words: extrdate, overall acceptability, response surface regression analysis, balanced incomplete block method.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.83-126
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• 2005

Single-index models have found applications in econometrics and biometrics, where multidimensional regression models are often encountered. Here we propose a nonparametric estimation approach that combines wavelet methods for non-equispaced designs with Bayesian models. We consider a wavelet series expansion of the unknown regression function and set prior distributions for the wavelet coefficients and the other model parameters. To ensure model identifiability, the direction parameter is represented via its polar coordinates. We employ ad hoc hierarchical mixture priors that perform shrinkage on wavelet coefficients and use Markov chain Monte Carlo methods for a posteriori inference. We investigate an independence-type Metropolis-Hastings algorithm to produce samples for the direction parameter. Our method leads to simultaneous estimates of the link function and of the index parameters. We present results on both simulated and real data, where we look at comparisons with other methods.

• 품질경영학회지
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• 제26권1호
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• 한국공간구조학회논문집
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• 제20권4호
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• pp.111-121
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• 2020

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

• Nuclear Engineering and Technology
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• 제49권7호
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• pp.1423-1430
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• 2017

Detonation cell width is an important parameter in hydrogen explosion assessments. The experimental data on gas detonation are statistically analyzed to establish a universal method to numerically predict detonation cell widths. It is commonly understood that detonation cell width, ${\lambda}$, is highly correlated with the characteristic reaction zone width, ${\delta}$. Classical parametric regression methods were widely applied in earlier research to build an explicit semiempirical correlation for the ratio of ${\lambda}/{\delta}$. The obtained correlations formulate the dependency of the ratio ${\lambda}/{\delta}$ on a dimensionless effective chemical activation energy and a dimensionless temperature of the gas mixture. In this paper, support vector regression (SVR), which is based on nonparametric machine learning, is applied to achieve functions with better fitness to experimental data and more accurate predictions. Furthermore, a third parameter, dimensionless pressure, is considered as an additional independent variable. It is found that three-parameter SVR can significantly improve the performance of the fitting function. Meanwhile, SVR also provides better adaptability and the model functions can be easily renewed when experimental database is updated or new regression parameters are considered.

• Communications for Statistical Applications and Methods
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• 제31권4호
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• pp.441-457
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• 2024

Semicontinuous data are characterized by a mixture of a point probability mass at zero and a continuous distribution of positive values. This type of data is often modeled using a two-part model where the first part models the probability of dichotomous outcomes -zero or positive- and the second part models the distribution of positive values. Despite the two-part model's popularity, variable selection in this model has not been fully addressed, especially, in high dimensional data. The objective of this study is to investigate variable selection and prediction performance of penalized regression methods in two-part models. The performance of the selected techniques in the two-part model is evaluated via simulation studies. Our findings show that LASSO and ENET tend to select more predictors in the model than SCAD and MCP. Consequently, MCP and SCAD outperform LASSO and ENET for β-specificity, and LASSO and ENET perform better than MCP and SCAD with respect to the mean squared error. We find similar results when applying the penalized regression methods to the prediction of crime incidents using community-based data.

• Human Ecology Research
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• 제55권1호
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• pp.13-26
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• 2017

This study investigated the latent groups depending on married working women's work-family spillover. The effects of factors that determine mental health subgroups and differences were also analyzed. Mixture modeling was applied to the Korean Longitudinal Survey of Women & Families to achieve the research objectives. The major findings of this study were as follows. First, there were four subgroups that could be defined according to the work-family spillover: mid-level spillover group (mid-positive and mid-negative spillover group), high-level spillover group (high-positive and high-negative spillover group), low-level spillover group (low-positive and low-negative spillover group), and high-negative and low-positive spillover group. Second, the results of mixture regression analysis to test the effect of eco-system variables showed that age, academic background, non-traditional family value, number of children, work hours, wage income, and availability of the maternity leave were significant determinants of the latent groups. The probability of classifying in the high-negative and low-positive spillover group increased when women showed a lower academic background and wage income, higher number of children and older age, and longer work hours than others. Third, the high-level spillover group, and the high-level spillover group showed the lowest stress and the lowest depression; however, the low-level spillover group reported the highest stress and the highest depression. Implications, limitations, and future directions were discussed based on the results.

• 한국자동차공학회논문집
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• 제18권1호
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• pp.74-85
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• 2010

In order to formulate the compounded polypropylene(C-PP) which is suitable to an automotive door trim panel, 9 sorts of properties were measured after manufacturing the C-PP using an extruder and an injection machine with polypropylene(PP), ethylene-octene rubber(EOR) and talc. Mixture design, especially extreme vertices design, in DOE with MINITAB - commercial software was used to analyze the data. The relations between each property and each component, for example, $y=0.00907222x_1+0.00870556x_2+0.0155722x_3$ for specific gravity, were found out by the regression analysis and the variance analysis. The optimized formulation of the C-PP for an automotive door trim panel was acquired at PP(77.6962), EOR(11.0238) and talc(10.2800) by use of the response optimizer(mixture) in MINITAB.

• 응용통계연구
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• 제21권1호
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• pp.151-157
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• 2008

수요예측은 모든 생산적 활동을 수립하기 위한 기반이 되기 때문에 수요가 어느 정도 발생할 것인가에 대한 방향성에 대하여 파악하려고 일반적으로 설문조사를 이용하지만 무응답 및 불성실한 응답으로 인하여 설문응답 자료만으로 수요를 예측하기에는 부족하다. 따라서 수요와 관련 있는 변수를 이용한 분류모형으로 설문조사의 수요예측을 보정하고자 하였다. 본 논문에서는 설문조사를 통하여 평가 할 수 있는 직접적인 수요와 통계적 모형을 이용한 간접적 수요를 혼합하여 서비스 수요를 예측하는 혼합 모형을 제시하고자 한다.