• Title/Summary/Keyword: least-squares regression analysis

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Partial Least Squares Analysis on Near-Infrared Absorbance Spectra by Air-dried Specific Gravity of Major Domestic Softwood Species

  • Yang, Sang-Yun;Park, Yonggun;Chung, Hyunwoo;Kim, Hyunbin;Park, Se-Yeong;Choi, In-Gyu;Kwon, Ohkyung;Cho, Kyu-Chae;Yeo, Hwanmyeong
    • Journal of the Korean Wood Science and Technology
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    • v.45 no.4
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    • pp.399-408
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    • 2017
  • Research on the rapid and accurate prediction of physical properties of wood using near-infrared (NIR) spectroscopy has attracted recent attention. In this study, partial least squares analysis was performed between NIR spectra and air-dried specific gravity of five domestic conifer species including larch (Larix kaempferi), Korean pine (Pinus koraiensis), red pine (Pinus densiflora), cedar (Cryptomeria japonica), and cypress (Chamaecyparis obtusa). Fifty different lumbers per species were purchased from the five National Forestry Cooperative Federations of Korea. The air-dried specific gravity of 100 knot- and defect-free specimens of each species was determined by NIR spectroscopy in the range of 680-2500 nm. Spectral data preprocessing including standard normal variate, detrend and forward first derivative (gap size = 8, smoothing = 8) were applied to all the NIR spectra of the specimens. Partial least squares analysis including cross-validation (five groups) was performed with the air-dried specific gravity and NIR spectra. When the performance of the regression model was expressed as $R^2$ (coefficient of determination) and root mean square error of calibration (RMSEC), $R^2$ and RMSEC were 0.63 and 0.027 for larch, 0.68 and 0.033 for Korean pine, 0.62 and 0.033 for red pine, 0.76 and 0.022 for cedar, and 0.79 and 0.027 for cypress, respectively. For the calibration model, which contained all species in this study, the $R^2$ was 0.75 and the RMSEC was 0.37.

Use of partial least squares analysis in concrete technology

  • Tutmez, Bulent
    • Computers and Concrete
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    • v.13 no.2
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    • pp.173-185
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    • 2014
  • Multivariate analysis is a statistical technique that investigates relationship between multiple predictor variables and response variable and it is a very commonly used statistical approach in cement and concrete industry. During model building stage, however, many predictor variables are included in the model and possible collinearity problems between these predictors are generally ignored. In this study, use of partial least squares (PLS) analysis for evaluating the relationships among the cement and concrete properties is investigated. This regression method is known to decrease the model complexity by reducing the number of predictor variables as well as to result in accurate and reliable predictions. The experimental studies showed that the method can be used in the multivariate problems of cement and concrete industry effectively.

Estimation of Regionai Skew Coefficient with Weighted Least Squares Regression (가중회귀분석에 의한 지역화왜곡계수의 추정)

  • 조국광;권순국
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.32 no.1
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    • pp.103-109
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    • 1990
  • The application of the Log-Pearson Type m distribution recommended by Water Resources Council, U. S. A. for flood frequency analysis requires the estimation of the regionalized skew coefficient. In this study, regionalized skew coefficients are estimated using a weighted regression model which relates at-site skews based on logarithms of observed annual flood peak series to both basin characteristics and precipitation data in the Han river and the Nakdong river basin. The model is developed with weighted least squares method in which the weights are determined by separating residual variance into that due to model error and due to sampling error. As the result of analysis, regionalized skews are estimated as - 0.732 and - 0.575 in the Han river and the Nakdong river basin, respectively.

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Near Infrared Spectroscopy of LAS (linear alkyl benzene sulfonate) (근적외선 분광분석법을 이용한 LAS (linear alkyl benzene sulfonate)의 정량분석법)

  • 조창희;최병기;김효진
    • Environmental Analysis Health and Toxicology
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    • v.15 no.1_2
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    • pp.39-43
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    • 2000
  • Linear alkyl sulfonates (LAS) constitute a large fraction of the surfactants used in cleaning processes in households, trade and industry Despite the industrial significance and the possible environmental impact of these compounds, the fast and inexpensive determination of LAS concentrations is still a difficult task. In this study, near infrared (NIR) spectroscopy which is a rapid spectroscopic analysis method compared with a traditional analytical method for the measurement of LAS concentration such as HPLC, GC and standard wet chemistry method. NIR spectra of LAS between 0.313 and 25.0% (w/v) in water were utilized to develop a calibration model. The best results (R = 0.998, SEP = 0.244% (w/v)) obtained by using partial least-squares regression with spectral data treatment and 2nd derivatization were comparable to the results (SEC = 0.186% (w/v), SEP = 0.206% (w/v)) obtained by using multiple linear least-squares regression (MLR). However, models based on derivative spectra have no significant advantage with MLR.

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Analysis of Carbonization Behavior of Hydrochar Produced by Hydrothermal Carbonization of Lignin and Development of a Prediction Model for Carbonization Degree Using Near-Infrared Spectroscopy (열수 탄화 공정을 거친 리그닌 하이드로차(hydrochar)의 탄화 거동 분석과 근적외선 분광법을 이용한 예측 모델 개발)

  • HWANG, Un Taek;BAE, Junsoo;LEE, Taekyeong;HWANG, Sung-Yun;KIM, Jong-Chan;PARK, Jinseok;CHOI, In-Gyu;KWAK, Hyo Won;HWANG, Sung-Wook;YEO, Hwanmyeong
    • Journal of the Korean Wood Science and Technology
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    • v.49 no.3
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    • pp.213-225
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    • 2021
  • In this paper, we investigated the carbonization characteristics of lignin hydrochar prepared by hydrothermal carbonization and established a model for predicting the carbonization degree using near-infrared spectroscopy and partial least squares regression. The carbon content of the hydrothermally carbonized lignin at the temperature of 200 ℃ was higher by approximately 3 wt% than that of the untreated sample, and the carbon content tended to gradually increase as the heating time increased. Hydrothermal carbonization made lignin more carbon-intensive and more homogeneous by eliminating the microparticles. The discriminant and predictive models using near-infrared spectroscopy and partial least squares regression approppriately determined whether hydrothermal carbonization has been applied and predicted the carbon content of hydrothermal carbonized lignin with high accuracy. In this study, we confirmed that we can quickly and nondestructively predict the carbonization characteristics of lignin hydrochar manufactured by hydrothermal carbonization using a partial least squares regression model combined with near-infrared spectroscopy.

Mixed effects least squares support vector machine for survival data analysis (생존자료분석을 위한 혼합효과 최소제곱 서포트벡터기계)

  • Hwang, Chang-Ha;Shim, Joo-Yong
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.4
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    • pp.739-748
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    • 2012
  • In this paper we propose a mixed effects least squares support vector machine (LS-SVM) for the censored data which are observed from different groups. We use weights by which the randomly right censoring is taken into account in the nonlinear regression. The weights are formed with Kaplan-Meier estimates of censoring distribution. In the proposed model a random effects term representing inter-group variation is included. Furthermore generalized cross validation function is proposed for the selection of the optimal values of hyper-parameters. Experimental results are then presented which indicate the performance of the proposed LS-SVM by comparing with a standard LS-SVM for the censored data.

On Parameter Estimation of Growth Curves for Technological Forecasting by Using Non-linear Least Squares

  • Ko, Young-Hyun;Hong, Seung-Pyo;Jun, Chi-Hyuck
    • Management Science and Financial Engineering
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    • v.14 no.2
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    • pp.89-104
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    • 2008
  • Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.

Correlation Between the Point-Load Strength and the Uniaxial Compressive Strength of Korean Granites (국내 화강암의 점하중강도와 일축압축강도간의 상관분석)

  • Woo, Ik
    • The Journal of Engineering Geology
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    • v.24 no.1
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    • pp.101-110
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    • 2014
  • This study presents the results of a regression analysis of the point-load strength ($I_{s(50)}$) and the uniaxial compressive strength (UCS) of granites in Korea. The regression was carried out for three cases using the least-squares method, reclassifying the granite samples based on their physical properties. The first regression analysis through the origin according to the weathering grade did not give a result with a sufficient degree of confidence, due to the small number of samples. However, the general trend of the correlation between UCS and $I_{s(50)}$according to weathering grade shows that the slope of the linear regression for weathered granite is steeper than that for fresh granite. The second analysis was a simple linear regression for all the granite samples using the least-squares method as well as a linear regression using the bootstrap resampling method in order to increase the confidence level and the accuracy of the regression results. The third regression considered the average strength of granite groups reclassified according to physical properties. These linear regression analyses yielded linear regression equations with slopes of 14 and small standard deviations being similar to values reported in previous studies on Korean granites, but whose intercept values range from 16 to 43 and have a larger standard deviation than those of the present study. In conclusion, it would be advisable to estimate UCS from $I_{s(50)}$, considering the error range derived from the deviation of the regression equations.

Bayesian Analysis for a Functional Regression Model with Truncated Errors in Variables

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • v.31 no.1
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    • pp.77-91
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    • 2002
  • This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

Population Pharmacokinetic Modeling of Vancomycin in Patients with Cancer (암환자에게 반코마이신의 집단약물동태학 모델연구)

  • 최준식;민영돈;범진필
    • YAKHAK HOEJI
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    • v.43 no.2
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    • pp.160-168
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    • 1999
  • The purpose of this study was to determine pharmacokinetic parameters of vancomycin using peak and trough plasma level (PTL) and Bayesian analysis in 20 Korean normal volunteers, 16 gastric cancer and 12 lymphoma patients and also using the compartment model dependent (nonlinear least squares regression: NLSR) and compartment model independent (Lagrange) analysis in 10 ovarian cancer patients. Nonparametric expected maximum (NPEM) algorithm for calculation of the population pharmacokinetic parameters was used, and these parameters were applied for clinical pharmacokinetic parameters by Bayesian analysis. Vancomycin was administered as dose of 1.0 g every 12 hrs for 3 days by IV infusion over 60 minutes in normal volunteers, gastric cancer and lymphoma patients. Population pharmacokinetic parameters, K and Vd in gastric cancer and lymphoma patients using NPEM algorithm were $0.158{\pm}0.014{\;}hr^{-1},{\;}0.630{\pm}0.043{\;}L/kg{\;}and{\;}0.131{\pm}0.0261{\;}hr^{-1},{\;}0.631{\pm}0.089{\;}L/kg$ respectively. The K and Vd in gastric cancer and lymphoma patients using Bayesian analysis were $0.151{\pm}0.027,{\;}0.126{\pm}0.056{\;}hr^{-1}{\;}and{\;}0.62{\pm}0.105,{\;}0.63{\pm}0.095{\;}L/kg$. The K and Vd in ovarian cancer patient using the NLSR and Lagrange analysis were $0.109{\pm}0.008,{\;}0.126{\pm}0.012{\;}hr^{-1}{\;}and{\;} 0.76{\pm}0.08,{\;}0.69{\pm}0.19{\;}L/kg$, respectively. It is necessary for effective dosage regimen of vancomycin in cancer patients to use these population parameters.

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