• Journal of the Korean Statistical Society
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• 제13권2호
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• pp.107-113
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• 1984

In a linear regression model the idependent variables are frequently subject to measurement errors. For this case, the problem of estimating unknown parameters has been extensively discussed in the literature while very few has been concerned with the effect of measurement errors on prediction. This paper investigates the behavior of the predicted values of the dependent variable in terms of the average mean square error of prediction (AMSEP). AMSEP may be used as a criterion for selecting an appropriate estimation method, for designing an estimation experiment, and for developing cost-effective future sampling schemes.

• 품질경영학회지
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• 제21권1호
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• pp.152-160
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• 1993

In developing a quadratic regression relationship, independent variable is frequently measured with error. In this paper the integrated mean square error of prediction is developed for a quadratic functional relationship model as a measure of the effect of measurement error of the independent variable on the predicted values. The amount of the effect of error is presented and illustrated with an example.

• Nuclear Engineering and Technology
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• 제42권2호
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• pp.163-174
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• 2010

As pointed out in the OECD BEMUSE Program, when a high computation time is taken to obtain the relevant output values of a complex physical model (or code), the number of statistical samples that must be evaluated through it is a critical factor for the sampling-based uncertainty analysis. Two alternative methods have been utilized to avoid the problem associated with the size of these statistical samples: one is based on Wilks' formula, which is based on simple random sampling, and the other is based on the conventional nonlinear regression approach. While both approaches provide a useful means for drawing conclusions on the resultant uncertainty with a limited number of code runs, there are also some unique corresponding limitations. For example, a conclusion based on the Wilks' formula can be highly affected by the sampled values themselves, while the conventional regression approach requires an a priori estimate on the functional forms of a regression model. The main objective of this paper is to assess the feasibility of the ACE-RSM approach as a complementary method to the Wilks' formula and the conventional regression-based uncertainty analysis. This feasibility was assessed through a practical application of the ACE-RSM approach to the LOFT L2-5 LBLOCA PCT uncertainty analysis, which was implemented as a part of the OECD BEMUSE Phase III program.

• Bulletin of the Korean Chemical Society
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• 제32권11호
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• pp.3865-3872
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• 2011

A quantitative structure-property relationship (QSPR) study was performed for the prediction of the absorption maxima of azobenzene dyes. The entire set of 191 azobenzenes was divided into a training set of 150 azobenzenes and a test set of 41 azobenzenes according to Kennard and Stones algorithm. A seven-descriptor model, with squared correlation coefficient ($R^2$) of 0.8755 and standard error of estimation (s) of 14.476, was developed by applying stepwise multiple linear regression (MLR) analysis on the training set. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-many-out crossvalidation procedure, randomization tests, and validation through the test set.

• 품질경영학회지
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• 제27권2호
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• pp.263-276
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• 1999

In this paper, we consider the change request data collected from the system test of a large-scale telecommunication software and analyze the types and causes of failures. And we develop statistical models that incorporate a functional relation between the faults and some software metrics. To this end, we consider three possible regression models including a stepwise regression model and two nonlinear models. Three developed models are evaluated with respect to the predictive quality. We also discuss the advantage of proposed models and the application of our model to a new project.

• 한국건설관리학회논문집
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• 제11권6호
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• pp.3-13
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• 2010

개산견적 모델은 예측의 정밀성뿐만 아니라 설계대안 변화에 대한 유연성, 사용자 중심의 효율성을 고려해야 한다. 본 연구에서는 이를 구현하기 위해 건축물을 구성하는 여러 포함시설을 건축양상에 맞게 주요 용도별로 분류하여 공사비 영향변수로 설정한 다음, 통계적 분석을 통해 용도별 시설단가 관계함수를 도출하여 공사비를 예측하는 용도별 분류에 의한 공사비 산정개념을 제안하였다. 그리고 대표적 군사시설인 육군 병영생활관을 대상으로 용도별 공사비 산정 개념을 모델화하고 신규사례를 대상으로 모델의 신뢰성을 검증하였다. 용도별 공사비는 견적의 정밀성을 향상시켰을 뿐만 아니라, 발주자 니즈(Needs)를 반영한 용도별 조합과 그 규모에 따른 맞춤형 공간을 계획할 수 있고 다양한 설계 대안에 대한 비용비교가 가능하다.

• Journal of the Korean Data and Information Science Society
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• 제25권4호
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• pp.941-949
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• 2014

Unit level logistic regression model with mixed effects has been used for estimating small area proportions, which treats the spatial effects as random effects and assumes linearity between the logistic link and the covariates. However, when the functional form of the relationship between the logistic link and the covariates is not linear, it may lead to biased estimators of the small area proportions. In this paper, we relax the linearity assumption and propose two types of kernel-based logistic regression models for estimating small area proportions. We also demonstrate the efficiency of our propose models using simulated data and real data.

• 한국안전학회지
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• 제24권2호
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• pp.94-99
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• 2009

This study deals with the traffic accidents at the 3-legged signalized intersections in Cheongu. The goals are to analyze the geometric, traffic and operational conditions of intersections and to develop a various functional forms that predict the accidents. The models are developed through the correlation analysis, the multiple linear, the multiple nonlinear, Poisson and negative binomial regression analysis. In this study, two multiple linear, two multiple nonlinear and two negative binomial regression models were calibrated. These models were all analyzed to be statistically significant. All the models include 2 common variables(traffic volume and lane width) and model-specific variables. These variables are, therefore, evaluated to be critical to the accident reduction of Cheongju.

• 지역사회간호학회지
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• 제17권3호
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• pp.346-353
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• 2006

Purpose: The Purpose of this study was to analyze factors influencing the dependence of the elderly. Method: This research was designed as a descriptive study. The subjects were 103 elders living in Seoul. Data were collected from December 2004 through January 2005. The instrument used in this study was the elderly dependence scale developed by the Park (2004). Collected data were analyzed using SPSS 11.0 for Windows. Descriptive statistics, independent t-test, ANOVA and multiple regression were done. Results: In the results of regression, residence, education, vision and age were extracted as factors influencing the dependence of the elderly. The regression model explained 33% of the variance. Conclusion: Considering the results above, we need studies on the dependence of the elderly using more independent variables, on the changing pattern of dependence and influencing factors by longitudinal design, and on the elderly with functional limitations or cognitive impairment.

• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.191-202
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• 2024

The goal of this paper is to show how multivariate regression analysis with high-dimensional responses is facilitated by the response dimension reduction. Multivariate regression, characterized by multi-dimensional response variables, is increasingly prevalent across diverse fields such as repeated measures, longitudinal studies, and functional data analysis. One of the key challenges in analyzing such data is managing the response dimensions, which can complicate the analysis due to an exponential increase in the number of parameters. Although response dimension reduction methods are developed, there is no practically useful illustration for various types of data such as so-called large p-small n data. This paper aims to fill this gap by showcasing how response dimension reduction can enhance the analysis of high-dimensional response data, thereby providing significant assistance to statistical practitioners and contributing to advancements in multiple scientific domains.