• Title/Summary/Keyword: Rank regression

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Sensitivity Analysis of Creep and Shrinkage Effects of Prestressed Concrete Bridges (프리스트레스트 콘크리트 교량의 크리프와 건조수축효과의 민감도 해석)

  • 오병환;양인환
    • Proceedings of the Korea Concrete Institute Conference
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    • 1998.10b
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    • pp.656-661
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    • 1998
  • This paper presents a method of statistical analysis and sensitivity analysis of creep and shrinkage effects in PSC box girder bridges. The statistical and sensitivity analyses are performed by using the numerical simulation of Latin Hypercube sampling. For each sample, the time-dependent structural analysis is performed to produce response data, which are then statistically analyzed. The probabilistic prediction of the confidence limits on long-term effects of creep and shrinkage is then expressed. Three measures are examined to quantify the sensitivity of the outputs to each of the input variables. These are rank correlation coefficient(RCC), partial rank correlation coefficient(PRCC) and standardized rank regression coefficient(SRRC) computed on the ranks of the observations. Probability band widens with time, which indicates an increase of prediction uncertainty with time. The creep model uncertainty factor and the relative humidity appear as the most dominant factors with regard to the model output uncertainty.

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Weighting Value Evaluation of Condition Assessment Item in Reinforced Earth Retaining Walls by Applying Hybrid Weighting Technique (혼합 가중치를 적용한 보강토 옹벽의 상태평가항목 가중치 평가)

  • Lee, Hyung Do;Won, Jeong-Hun;Seong, Joohyun
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.21 no.5
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    • pp.83-93
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    • 2017
  • This study proposed the new weighting values and fault points of condition assessment items for reinforced earth retaining walls based on the combination the inspection data and hybrid weighting technique. Utilizing the inspection data of 161 reinforced earth retaining walls, multi regression analysis and entropy technique were applied to gain the weighting values of condition assessment items. In addition, the weighting values by AHP technique was analyzed based on the opinion of experts. By appling hybrid weighting technique to the calculated weighting values obtained by the individual technique, the new weighting values of condition assessment items were proposed, and the fault points and fault indices of reinforced earth retaining walls were proposed. Results showed that the rank of the weighting value of the condition evaluation items was fluctuated according to the multiple regression analysis, AHP technique, and entropy technique. There was no duplication of the rank of the weighting value while the current weighting value was overlapped. Specially, in the rsults of multi regression analysis, two condition assessment items were occupied 70% of the total weights. When the proposed weighting values were applied to existing reinforced earth retaining wall of 161, 16 reinforced earth retaining walls showed the increased risk rank and 31 represented the decreased risk rank.

Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies -

  • Nam, Youn Chang;Lee, Kun Chang
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.4
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    • pp.63-71
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    • 2016
  • This paper is about applying efficient data mining method which improves the score calculation and proper building performance of credit ranking score system. The main idea of this data mining technique is accomplishing such objectives by applying Correlation based Feature Selection which could also be used to verify the properness of existing rank scores quickly. This study selected 2047 manufacturing companies on KOSPI market during the period of 2009 to 2013, which have their own credit rank scores given by NICE information service agency. Regarding the relevant financial variables, total 80 variables were collected from KIS-Value and DART (Data Analysis, Retrieval and Transfer System). If correlation based feature selection could select more important variables, then required information and cost would be reduced significantly. Through analysis, this study show that the proposed correlation based feature selection method improves selection and classification process of credit rank system so that the accuracy and credibility would be increased while the cost for building system would be decreased.

A Study on the Consumers' Expectation, Perception, Quality, and Satisfaction with the Industrial Nursing Services (간호서비스에 대한 대상자의 기대와 지각, 만족에 관한 연구 -SERVQUAL 척도의 적용-)

  • Jung, Myun-Sook;Youn, Mi-Jin
    • Research in Community and Public Health Nursing
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    • v.12 no.3
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    • pp.570-581
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    • 2001
  • The purpose of this study was to analyze consumers' expectation. perception and satisfaction. The subjects in this study were 200 employees who were employed in a department store. They experienced nursing services from the primary care center at the department store. The results were as follows: 1. The rank of perceived significance of nursing services is tangibility, reliability, assurance, responsiveness. and empathy, respectively. 2. The rank of perceived service quality is empathy. responsiveness. assurance. reliability. and tangibility. 3. The rank of expectation and the rank of perception about nursing services are same. They are assurance, reliability, responsiveness, tangibility, and empathy respectively. 4. The regression analysis, which related the effect of perceived service quality to consumers satisfaction, had R2 value of 22.9%. From the above results, it can be concluded that the higher consumers' satisfaction can be explained by the greater perceived service quality.

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Comparative Study on Statistical Packages Analyzing Survival Model - SAS, SPSS, STATA -

  • Cho, Mi-Soon;Kim, Soon-Kwi
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.2
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    • pp.487-496
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    • 2008
  • Recently survival analysis becomes popular in a variety of fields so that a number of statistical packages are developed for analyzing the survival model. In this paper, several types of survival models are introduced and considered briefly. In addition, widely used three packages(SAS, SPSS, and STATA) for survival data are reviewed and their characteristics are investigated.

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A Nonparametric Method for Nonlinear Regression Parameters

  • Kim, Hae-Kyung
    • Journal of the Korean Statistical Society
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    • v.18 no.1
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    • pp.46-61
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    • 1989
  • This paper is concerned with the development of a nonparametric procedure for the statistical inference about the nonlinear regression parameters. A confidence region and a hypothesis testing procedure based on a class of signed linear rank statistics are proposed and the asymptotic distributions of the test statistic both under the null hypothesis and under a sequence of local alternatives are investigated. Some desirable asymptotic properties including the asymptotic relative efficiency are discussed for various score functions.

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Testing General Linear Constraints on the Regression Coefficient Vector : A Note

  • Jeong, Ki-Jun
    • Journal of the Korean Statistical Society
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    • v.8 no.2
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    • pp.107-109
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    • 1979
  • Consider a linear model with n observations and k explanatory variables: (1)b $y=X\beta+u, u\simN(0,\sigma^2I_n)$. We assume that the model satisfies the ideal conditions. Consider the general linear constraints on regression coefficient vector: (2) $R\beta=r$, where R and r are known matrices of orders $q\timesk$ and q\times1$ respectively, and the rank of R is $qk+q$.

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Imputation Method Using Local Linear Regression Based on Bidirectional k-nearest-components

  • Yonggeol, Lee
    • Journal of information and communication convergence engineering
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    • v.21 no.1
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    • pp.62-67
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    • 2023
  • This paper proposes an imputation method using a bidirectional k-nearest components search based local linear regression method. The bidirectional k-nearest-components search method selects components in the dynamic range from the missing points. Unlike the existing methods, which use a fixed-size window, the proposed method can flexibly select adjacent components in an imputation problem. The weight values assigned to the components around the missing points are calculated using local linear regression. The local linear regression method is free from the rank problem in a matrix of dependent variables. In addition, it can calculate the weight values that reflect the data flow in a specific environment, such as a blackout. The original missing values were estimated from a linear combination of the components and their weights. Finally, the estimated value imputes the missing values. In the experimental results, the proposed method outperformed the existing methods when the error between the original data and imputation data was measured using MAE and RMSE.

Development of Customer Review Ranking Model Considering Product and Service Aspects Using Random Forest Regression Method

  • Arif Djunaidy;Nisrina Fadhilah Fano
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.8
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    • pp.2137-2156
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    • 2024
  • Customer reviews are the second-most reliable source of information, followed by family and friend referrals. However, there are many existing customer reviews. Some online shopping platforms address this issue by ranking customer reviews according to their usefulness. However, we propose an alternative method to rank customer reviews, given that this system is easily manipulable. This study aims to create a ranking model for reviews based on their usefulness by combining product and seller service aspects from customer reviews. This methodology consists of six primary steps: data collection and preprocessing, aspect extraction and sentiment analysis, followed by constructing a regression model using random forest regression, and the review ranking process. The results demonstrate that the ranking model with service considerations outperformed the model without service considerations. This demonstrates the model's superiority in the three tests, which include a comparison of the regression results, the aggregate helpfulness ratio, and the matching score.

Asymptotic Relative Efficiency for New Score Functions in Rank Regression Models (순위회귀모형의 새로운 스코어 함수의 효율성 연구)

  • 최영훈
    • The Korean Journal of Applied Statistics
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    • v.17 no.2
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    • pp.269-280
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    • 2004
  • We explore the selection of r and s that provides improvement over the Wilcoxon scores under the asymmetric distributions we encounter in practice. We select 0 〈 r 〈 1, s 〉 1 for right-skewed distribution and r 〉 1,0 〈 s 〈 1 for left-skewed distributions from the perspective plots. We also study the association between the desirable r and s and the test statistic for skewness.