• Title, Summary, Keyword: Standard Error of Prediction

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Model Selection for Tree-Structured Regression

  • Kim, Sung-Ho
    • Journal of the Korean Statistical Society
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    • v.25 no.1
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    • pp.1-24
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    • 1996
  • In selecting a final tree, Breiman, Friedman, Olshen, and Stone(1984) compare the prediction risks of a pair of tree, where one contains the other, using the standard error of the prediction risk of the larger one. This paper proposes an approach to selection of a final tree by using the standard error of the difference of the prediction risks between a pair of trees rather than the standard error of the larger one. This approach is compared with CART's for simulated data from a simple regression model. Asymptotic results of the approaches are also derived and compared to each other. Both the asymptotic and the simulation results indicate that final trees by CART tend to be smaller than desired.

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The relationship between prediction accuracy and pre-information in collaborative filtering system

  • Kim, Sun-Ok
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.4
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    • pp.803-811
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    • 2010
  • This study analyzes the characteristics of preference ratings by dividing estimated values into four groups according to rank correlation coefficient after obtaining preference estimated value to user's ratings by using collaborative filtering algorithm. It is known that the value of standard error of skewness and standard error of kurtosis lower in the group of higher rank correlation coefficient This explains that the preference of higher rank correlation coefficient has lower extreme values and the differences of preference rating values. In addition, top n recommendation lists are made after obtaining rank fitting by using the result ranks of prediction value and the ranks of real rated values, and this top n is applied to the four groups. The value of top n recommendation is calculated higher in the group of higher rank correlation coefficient, and the recommendation accuracy in the group of higher rank correlation coefficient is higher than that in the group of lower rank correlation coefficient Thus, when using standard error of skewness and standard error of kurtosis in recommender system, rank correlation coefficient can be higher, and so the accuracy of recommendation prediction can be increased.

Creep Life Prediction and Error Analysis for Type 316LN Stainless Steel (Type 316LN 스테인리스강의 크리프 수명예측과 오차분석)

  • Yi W.;Yin S.N.;Kim W.G.;Ryu W.S.
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.109-110
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    • 2006
  • Various parametric methods, Larson-Miller (L-M), Orr-Sherby-Dorn (O-S-D), Manson-Haferd (M-H) parameters, and minimum commitment method (MCM), were used to predict longer rupture time from short-term creep data. A number of the creep data were collected through literature surveys and experimental data produced in KAERI for predicting the creep type of type 316LN SS. Polynomial equations for predicting the creep life were obtained by the time-temperature parameters (TTP) and the MCM. standard error (SE) and standard error or mean (SEM) values were compared for the each method with temperatures. The TTP methods were good in the creep-life prediction, but the MCM was much superior to the TTP ones at $700^{\circ}C\;and\;750^{\circ}C$. The MCM was found to be lower in the SE values compared to the TTP methods

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Large-sample comparisons of calibration procedures when both measurements are subject to error

  • Lee, Seung-Hoon;Yum, Bong-Jin
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • pp.254-262
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    • 1990
  • A predictive functional relationship model is presented for the calibration problem in which the standard as well as the nonstandard measurements are subject to error. For the estimation of the relationship between the two measurements, the ordinary least squares and maximum likelihood estimation methods are considered, while for the prediction of unknown standard measurementswe consider direct and inverse approaches. Relative performances of those calibration procedures are compared in terms of the asymptotic mean square error of prediction.

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Standard Error Analysis of Creep-Life Prediction Parameters of Type 316LN Stainless Steels (Type 316LN 강의 크리프 수명예측 파라메타의 표준오차 분석)

  • Kim, Woo-Gon;Yoon, Song-Nam;Ryu, Woo-Seog
    • Proceedings of the KSME Conference
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    • pp.19-24
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    • 2004
  • A number of creep data were collected and filed for type 316LN stainless steels through literature survey and experimental data produced in KAERI. Using these data, polynomial equations for predicting creep life were obtained for Larson Miller (L-M), Qrr-Sherby-Dorn (O-S-D) and Manson-Haferd (M-H) parametric methods. In order to find out the suitability for them, the relative standard error (RSE) and standard error of estimate (SEE) values were obtained by statistical process of creep data. The O-S-D parameter showed better fitting to creep-rupture data than the L-M or the M-H parameters, and the three parametric methods did not generate the large difference in the SEE and the RSE values.

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Application for Measuring the Glucose, Ammonia nitrogen, and Tylosin Concentration using Near Infrared Spectroscopy

  • Kim, Jong-Soo;Cho, Hoon
    • Journal of environmental and Sanitary engineering
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    • v.23 no.2
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    • pp.19-25
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    • 2008
  • For measurement of tylosin, ammonia nitrogen, and glucose concentration during the culture of Streptomyces fradiae using Near Infrared Spectroscopy, the calibration using various mathematical models was performed and then, based on the linear model, the validation was carried out. In the case of sucrose concentration using the MLR method, the Standard Error of Prediction and Multiple correlation coefficient were 1.97, and 0.991, respectively. In the case of ammonia nitrogen concentration using the PLSR method, the Standard Error of Prediction and Multiple correlation coefficient were 0.13, and 0.990, respectively. In the case of tylosin concentration using the PLSR method, the standard Error of Prediction and Multiple correlation coefficient were 0.54, and 0.984, respectively.

Evaluation of the Effect of Errors in Job Characteristics on the Predicted Total Task Time in Standard Data Systems (표준자료 산출시 작업특성치의 오차가 총작업시간의 예측에 미치는 영향평가)

  • Byun, Jai-Hyun;Yum, Bong-Jin
    • Journal of Korean Institute of Industrial Engineers
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    • v.17 no.2
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    • pp.97-105
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    • 1991
  • In developing a regression relationship for a standard data system in work measurement, job characteristics are frequently measured with error when measurements are made in the field under less controlled conditions or when accurate instruments are not available. This paper concerns with the prediction of the total task time when job characteristics are measured with error. Integrated mean square error of prediction(IMSE) is developed as a measure of the effect of errors in job characteristics on the predicted total task time. By evaluating how IMSE is affected by the measurement error in each job characteristic, we can determine which error should be controlled to develop a desirable standard data system.

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The Prediction of Compressive Strength and Slump Value of Concrete Using Neural Networks (신경망을 이용한 콘크리트의 압축강도 및 슬럼프값 추정)

  • Choi, Young-Wha;Kim, Jong-In;Kim, In-Soo
    • Journal of the Korean Society of Industry Convergence
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    • v.5 no.2
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    • pp.103-110
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    • 2002
  • An artificial neural network is applied to the prediction of compressive strength, slump value of concrete. Standard mixed tables arc trained and estimated, and the results are compared with those of experiments. To consider the varieties of material properties, the standard mixed tables of two companies of Ready Mixed Concrete are used. And they are trained with the neural network. In this paper, standard back propagation network is used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of 210, $240kgf/cm^2$ and target slump value of 12, 15cm are used because the amount of production of that range arc the most at ordinary companies. In results, in the prediction of compressive strength and slump value, the predicted values are converged well to those of standard mixed tables at the target error of 0.10, 0.05, 0.001 regardless of two companies.

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Simultaneous Determination of Tryptophan and Tyrosine by Spectrofluorimetry Using Multivariate Calibration Method (다변량 분석법을 이용한 Tryptophan과 Tyrosine의 형광분광법적 정량)

  • Lee, Sang-Hak;Park, Ju-Eun;Son, Beom-Mok
    • Journal of the Korean Chemical Society
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    • v.46 no.4
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    • pp.309-317
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    • 2002
  • A spectrofluorimetric method for the simultaneous determination of amino acids (tryptophan and tyrosine) based on the application of multivariate calibration method such as principal component regression and partial least squares (PLS) to luminescence measurements has been studied. Emission spectra of synthetic mixtures of two amino acids were obtained at excitation wavelength of 257 ㎚. The calibration model in PCR and PLS was obtained from the spectral data in the range of 280-500 ㎚ for each standard of a calibration set of 32 standards, each containing different amounts of two amino acids. The relative standard error of prediction ($RSEP_a$) was obtained to assess the model goodness in quantifying each analyte in a validation set. The overall relative standard error of prediction ($RSEP_m$) for the mixture obtained from the results of a validation set, formed by 6 independent mixtures was also used to validate the present method.

Bayesian inference in finite population sampling under measurement error model

  • Goo, You Mee;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.6
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    • pp.1241-1247
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    • 2012
  • The paper considers empirical Bayes (EB) and hierarchical Bayes (HB) predictors of the finite population mean under a linear regression model with measurement errors We discuss how to calculate the mean squared prediction errors of the EB predictors using jackknife methods and the posterior standard deviations of the HB predictors based on the Markov Chain Monte Carlo methods. A simulation study is provided to illustrate the results of the preceding sections and compare the performances of the proposed procedures.