• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.179-189
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• 2024

In multivariate regression with a high-dimensional response Y ∈ ℝ^r and a relatively low-dimensional predictor X ∈ ℝ^p (where r ≥ 2), the statistical analysis of such data presents significant challenges due to the exponential increase in the number of parameters as the dimension of the response grows. Most existing dimension reduction techniques primarily focus on reducing the dimension of the predictors (X), not the dimension of the response variable (Y). Yoo and Cook (2008) introduced a response dimension reduction method that preserves information about the conditional mean E(Y | X). Building upon this foundational work, Yoo (2018) proposed two semi-parametric methods, principal response reduction (PRR) and principal fitted response reduction (PFRR), then expanded these methods to unstructured principal fitted response reduction (UPFRR) (Yoo, 2019). This paper reviews these four response dimension reduction methodologies mentioned above. In addition, it introduces the implementation of the mbrdr package in R. The mbrdr is a unique tool in the R community, as it is specifically designed for response dimension reduction, setting it apart from existing dimension reduction packages that focus solely on predictors.

• 한국멀티미디어학회논문지
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• 제17권7호
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• pp.858-865
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• 2014

In the case of system like MES, various sensors collect the data in real time and save it as a big data to monitor the process. However, if there is big data mining in distributed computing system, whole processing process can be improved. In this paper, system to analyze the cause of operation deviation was built using the big data which has been collected from deasphalting process at the two different plants. By applying multivariate statistical analysis to the big data which has been collected through MES(Manufacturing Execution System), main cause of operation deviation was analyzed. We present the example of analyzing the operation deviation of deasphalting process using the big data which collected from MES by using multivariate statistics analysis method. As a result of regression analysis of the forward stepwise method, regression equation has been found which can explain 52% increase of performance compare to existing model. Through this suggested method, the existing petrochemical process can be replaced which is manual analysis method and has the risk of being subjective according to the tester. The new method can provide the objective analysis method based on numbers and statistic.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.791-800
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• 1999

Gross and Lai(1996) proposed a new approach for ordinary regression with left-truncated and right-censored (I.t.r.c) data. This paper shows how to apply nonparametric algorithms such as multivariate adaptive regression splines to 1.t.r.c data.

• 한국조사연구학회:학술대회논문집
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• 한국조사연구학회 2005년도 추계학술대회 발표논문집
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• pp.169-183
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• 2005

가중치 조정(weighting adjustment)으로 단위 무응답(unit nonresponse)을 처리하는 문제에서 성향점수를 추정하는 모형을 만들기 위해 응답변수와 관심변수를 동시에 고려하는 다변량 회귀나무(multivariate regression tree)기법을 제안하였다. 효과적인 무응답 조정층 구축을 위해 응답한 개체들만 사용하는 경우와 모든 개체들을 사용하는 경우를 제시하고 이 두방법을 편향의 관점으로 비교한다.

• Communications for Statistical Applications and Methods
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• 제11권2호
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• pp.381-397
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• 2004

The marginal likelihood has become an important tool for model selection in Bayesian analysis because it can be used to rank the models. We discuss the marginal likelihood for Poisson regression models that are potentially useful in small area estimation. Computation in these models is intensive and it requires an implementation of Markov chain Monte Carlo (MCMC) methods. Using importance sampling and multivariate density estimation, we demonstrate a computation of the marginal likelihood through an output analysis from an MCMC sampler.

• Journal of Korea Artificial Intelligence Association
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• 제1권2호
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• pp.7-13
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• 2023

In this paper, Depression is a mental disorder characterized by a lack of enthusiasm and feelings of sadness, which significantly impairs daily functioning. In 2018, there was an increase in book sales in the essay genre, particularly the popularity of "healing essays." This trend is seen as challenging the negative image and prejudices associated with depression. In 2021, a significant rise in the proportion of 20-year-old patients with depression is attributed to factors like job-related stress, interpersonal issues, and financial burdens. Additionally, there is a strong correlation between depression and suicidal thoughts, particularly among individuals who have experienced feelings of depression. Despite the increasing prevalence of depression among young adults, research in this area is lacking. To address this gap, statistical tools such as logistic regression and chi-squared tests are employed. The analysis reveals various independent variables associated with feelings of depression, shedding light on the relationships between these factors.

• Computers and Concrete
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• 제13권4호
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• pp.531-545
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• 2014

The purpose of this paper is to develop a prediction model for the compressive strength of waste LCD glass applied in concrete by analyzing a series of laboratory test results, which were obtained in our previous study. The hyperbolic function was used to perform the nonlinear-multivariate regression analysis of the compressive strength prediction model with the following parameters: water-binder ratio w/b, curing age t, and waste glass content G. According to the relative regression analysis, the compressive strength prediction model is developed. The calculated results are in accord with the laboratory measured data, which are the concrete compressive strengths of different mix proportions. In addition, a coefficient of determination $R^2$ value between 0.93 and 0.96 and a mean absolute percentage error MAPE between 5.4% and 8.4% were obtained by regression analysis using the predicted compressive analysis value, and the test results are also excellent. Therefore, the predicted results for compressive strength are highly accurate for waste LCD glass applied in concrete. Additionally, this predicted model exhibits a good predictive capacity when employed to calculate the compressive strength of washed glass sand concrete.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.101-113
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• 2002

In this paper we introduce a new score generating (unction for the rank regression in the linear regression model. The score function compares the $\gamma$'th and s\`th power of the tail probabilities of the underlying probability distribution. We show that the rank estimate asymptotically converges to a multivariate normal. further we derive the asymptotic Pitman relative efficiencies and the most efficient values of $\gamma$ and s under the symmetric distribution such as uniform, normal, cauchy and double exponential distributions and the asymmetric distribution such as exponential and lognormal distributions respectively.

• Archives of Pharmacal Research
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• 제11권1호
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• pp.45-51
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• 1988

The feature of resolution enhancement by derivative operation is linked to one of the multivariate analysis, which is multiple linear regression with two options, all possible and stepwise regression. Examined samples were synthetic mixtures of 5 vitamins, thiamine mononitrate, riboflavin phosphate, nicotinamide, pyridoxine hydrochloride and ascorbic acid. All components in mixture were quantified with reasonably good accuracy and precision. Whole data processing procedure was accomplished on-line by the development of three computer programs written in APPLESOFT BASIC language.

• 산업기술연구
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• 제22권A호
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• pp.201-210
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• 2002

The object of this study is to evaluate the factors affecting the water quality and to propose the influence of dominant factor quantitatively. The correlation analysis was performed to know the correlationship among the water quality items As a result of partial correlation analysis, it was shown that the water quality items are affected by the rainfall item directly. The factor analysis was performed to grasp some number of factors on each point for deducing the items of similar variable characteristics. The four points were divided into different factor groups. It was grasped that $NH_3-N$ and $NO_3-N$ Items have different variable characteristics after comparing the items. The Multiple regression analysis can decrease the number of observation. In the deduced multiple regression formula, it was shown that the rate of T-N, $NH_3-N$ and $NO_3-N$ in the independent variable took about 60% among all the regression formulas.