• Title/Summary/Keyword: Statistical prediction

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REGRESSION WITH CENSORED DATA BY LEAST SQUARES SUPPORT VECTOR MACHINE

  • Kim, Dae-Hak;Shim, Joo-Yong;Oh, Kwang-Sik
    • Journal of the Korean Statistical Society
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    • v.33 no.1
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    • pp.25-34
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    • 2004
  • In this paper we propose a prediction method on the regression model with randomly censored observations of the training data set. The least squares support vector machine regression is applied for the regression function prediction by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed prediction method.

The Development of Ensemble Statistical Prediction Model for Changma Precipitation (장마 강수를 위한 앙상블 통계 예측 모델 개발)

  • Kim, Jin-Yong;Seo, Kyong-Hwan
    • Atmosphere
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    • v.24 no.4
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    • pp.533-540
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    • 2014
  • Statistical forecast models for the prediction of the summertime Changma precipitation have been developed in this study. As effective predictors for the Changma precipitation, the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1), the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in Lee and Seo (2013). To further improve the performance of the statistical prediction scheme, we select other potential predictors and construct 2 additional statistical models. The selected predictors are the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies, and the spring Eurasian snow cover anomaly (EUSC). Then, using the total three statistical prediction models, a simple ensemble-mean prediction is performed. The resulting correlation skill score reaches as high as ~0.90 for the last 21 years, which is ~16% increase in the skill compared to the prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to a strengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO predictor induces the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation.

Statistical Prediction of False Alarm Rates in Automatic Vision Inspection System (자동결함 검출시스템에서 결함크기 측정오차로 인한 오검률의 통계적 예측)

  • Joo, Young-Bok;Huh, Kyung-Moo;Park, Kil-Houm
    • Journal of Institute of Control, Robotics and Systems
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    • v.15 no.9
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    • pp.906-908
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    • 2009
  • AVI (Automatic Vision Inspection) systems automatically detect defect features and measure their sizes via camera vision. It is important to predict the performance of an AVI to meet customer's specification in advance. Also the prediction can indicate the level of current performance of an AVI system. In this paper, we propose a statistical method for prediction of false alarm rate regarding inconsistency of defect size measurement process. For this purpose, only simple experiments are needed to measure the defect sizes for certain number of times. The statistical features from the experiment are utilized in the prediction process. Therefore, the proposed method is swift and easy to implement and use. The experiment shows a close prediction compared to manual inspection results.

Choice of Statistical Calibration Procedures When the Standard Measurement is Also Subject to Error

  • Lee, Seung-Hoon;Yum, Bong-Jin
    • Journal of the Korean Statistical Society
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    • v.14 no.2
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    • pp.63-75
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    • 1985
  • This paper considers a statistical calibration problem in which the standard as wel as the nonstandard measurement is subject to error. Since the classicla approach cannot handle this situation properly, a functional relationship model with additional feature of prediction is proposed. For the analysis of the problem four different approaches-two estimation techniques (ordinary and grouping least squares) combined with two prediction methods (classical and inverse prediction)-are considered. By Monte Carlo simulation the perromance of each approach is assessed in term of the probability of concentration. The simulation results indicate that the ordinary least squares with inverse prediction is generally preferred in interpolation while the grouping least squares with classical prediction turns out to be better in extrapolation.

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Statistical Prediction of False Alarm Rates in Automatic Vision Inspection System (결함크기 측정오차로 인한 오검률의 통계적 예측)

  • Joo, Young-Bok;Huh, Kyung-Moo;Park, Kil-Houm;Lee, Gyu-Bong;Han, Chan-Ho
    • Proceedings of the IEEK Conference
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    • 2009.05a
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    • pp.163-165
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    • 2009
  • Automatic Vision Inspection(AVI) systems automatically detect defect features and measure their sizes via camera vision. It is important to predict the performance of an AVI to meet customer's specification in advance. In this paper, we propose a statistical method for prediction of false alarm rate regarding inconsistency of defect size measuremet process. We only need are a simple experimental trial for repeated defect size measurement test. The statistical features from the experiement are utilized in the prediction process. Therefore, the proposed method is swift and easy to implement and use. The experiment shows a close prediction compared to manual inspection results.

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USING AN ABSTRACTION OF AMINO ACID TYPES TO IMPROVE THE QUALITY OF STATISTICAL POTENTIALS FOR PROTEIN STRUCTURE PREDICTION

  • Lee, Jin-Woo
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.15 no.3
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    • pp.191-199
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    • 2011
  • In this paper, we adopt a position specific scoring matrix as an abstraction of amino acid type to derive two new statistical potentials for protein structure prediction, and investigated its effect on the quality of the potentials compared to that derived using residue specific amino acid identity. For stringent test of the potential quality, we carried out folding simulations of 91 residue A chain of protein 2gpi, and found unexpectedly that the abstract amino acid type improved the quality of the one-body type statistical potential, but not for the two-body type statistical potential which describes long range interactions. This observation could be effectively used when one develops more accurate potentials for structure prediction, which are usually involved in merging various one-body and many-body potentials.

Variability of Short Term Creep Rupture Time and Life Prediction in Stainless Steels (스테인리스 강의 단시간 크리프 파단시간의 변동성과 수명예측)

  • Jung, Won-Taek;Kong, Yu-Sik;Kim, Seon-Jin
    • Journal of Ocean Engineering and Technology
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    • v.24 no.6
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    • pp.97-102
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    • 2010
  • This paper deals with the variability of short term creep rupture time based on previous creep rupture tests and the statistical methodology of the creep life prediction. The results of creep tests performed using constant uniaxial stresses at 600, 650, and $700^{\circ}C$ elevated temperatures were used for a statistical analysis of the inter-specimen variability of the short term creep rupture time. Even under carefully controlled identical testing conditions, the observed short-term creep rupture time showed obvious inter-specimen variability. The statistical aspect of the short term creep rupture time was analyzed using a Weibull statistical analysis. The effect of creep stress on the variability of the creep rupture time was decreased with an increase in the stress level. The effect of the temperature on the variability also decreased with increasing temperature. A long term creep life prediction method that considers this statistical variability is presented. The presented method is in good agreement with the Lason-Miller Parameter (LMP) life prediction method.

Prediction of extreme PM2.5 concentrations via extreme quantile regression

  • Lee, SangHyuk;Park, Seoncheol;Lim, Yaeji
    • Communications for Statistical Applications and Methods
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    • v.29 no.3
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    • pp.319-331
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    • 2022
  • In this paper, we develop a new statistical model to forecast the PM2.5 level in Seoul, South Korea. The proposed model is based on the extreme quantile regression model with lasso penalty. Various meteorological variables and air pollution variables are considered as predictors in the regression model, and the lasso quantile regression performs variable selection and solves the multicollinearity problem. The final prediction model is obtained by combining various extreme lasso quantile regression estimators and we construct a binary classifier based on the model. Prediction performance is evaluated through the statistical measures of the performance of a binary classification test. We observe that the proposed method works better compared to the other classification methods, and predicts 'very bad' cases of the PM2.5 level well.

PREDICTION OF 23RD SOLAR CYCLE USING THE STATISTICAL AND PRECURSOR METHOD (통계 및 프리커서 방법을 이용한 제23주기 태양활동예보)

  • JANG SE JIN;KIM KAP-SUNG
    • Publications of The Korean Astronomical Society
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    • v.14 no.2
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    • pp.91-102
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    • 1999
  • We have made intensive calculations on the maximum relative sunspot number and the date of solar maximum of 23rd solar cycle, by using the statistical and precursor methods to predict solar activity cycle. According to our results of solar data processing by statistical method, solar maximum comes at between February and July of 2000 year and at that time, the smoothed sunspot number will reach to $114.3\~122.8$. while precursor method gives rather dispersed value of $118\~17$ maximum sunspot number. It is found that prediction by statistical method using smoothed relative sunspot number is more accurate than by any method to use any data of 10.7cm radio fluxes and geomagnetic aa, Ap indexes, from the full analysis of solar cycle pattern of these data. In fact, current ascending pattern of 23rd solar cycle supports positively our predicted values. Predicted results by precursor method for $Ap_{avg},\;aa_{31-36}$ indexes show similar values to those by statistical method. Therefore, these indexes can be used as new precursors for the prediction of 23rd or next solar cycle.

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Bayesian Typhoon Track Prediction Using Wind Vector Data

  • Han, Minkyu;Lee, Jaeyong
    • Communications for Statistical Applications and Methods
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    • v.22 no.3
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    • pp.241-253
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    • 2015
  • In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.