• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.475-484
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• 2004

This paper consider the testing problem of grouped heteroscedasticity in the linear regression model. We provide the Lagrange Multiplier(LM), Wald, Likelihood Ratio (LR) test statistis for testing of grouped heteroscedasticity. Monte Carlo experiments are conducted to study the performance of these tests.

• Computers and Concrete
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• v.11 no.3
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• pp.237-252
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• 2013

This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.

• Structural Engineering and Mechanics
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• v.46 no.6
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• pp.853-868
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• 2013

The comparison of the effectiveness of artificial neural network (ANN) and linear regression (LR) in the prediction of strain in tie section using experimental data from eight high-strength-self-compact-concrete (HSSCC) deep beams are presented here. Prior to the aforementioned, a suitable ANN architecture was identified. The format of the network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of eleven and ten neurons in first and second TRAINLM training function was highly accurate and generated more precise tie strain diagrams compared to classical LR. The ANN's MSE values are 90 times smaller than the LR's. The correlation coefficient value from ANN is 0.9995 which is indicative of a high level of confidence.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.411-433
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• 2004

The shrinkage preliminary test ridge regression estimators (SPTRRE) based on Wald (W), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests for estimating the regression parameters of the multiple linear regression model with multivariate Student's t error distribution are considered in this paper. The quadratic biases and risks of the proposed estimators are compared under both null and alternative hypotheses. It is observed that there is conflict among the three estimators with respect to their risks because of certain inequalities that exist among the test statistics. In the neighborhood of the restriction, the SPTRRE based on LM test has the smallest risk followed by the estimators based on LR and W tests. However, the SPTRRE based on W test performs the best followed by the LR and LM based estimators when the parameters move away from the subspace of the restrictions. Some tables for the maximum and minimum guaranteed efficiency of the proposed estimators have been given, which allow us to determine the optimum level of significance corresponding to the optimum estimator among proposed estimators. It is evident that in the choice of the smallest significance level to yield the best estimator the SPTRRE based on Wald test dominates the other two estimators.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.611-624
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• 2010

Several methods were suggested for constructing a fuzzy relationship between fuzzy independent and dependent variables. This paper reviews the use of the method by minimizing the square of the difference between an observed and a predicted fuzzy number in an ${\alpha}$-level linear regression model. We introduce a new distance between fuzzy numbers on the basis of a mode, a core point and a radius of an ${\alpha}$-level set of a fuzzy number an construct the fuzzy regression model using the proposed fuzzy distance. We also investigate sufficient condition for an equivalence in the ${\alpha}$-level regression model.

• The Korean Journal of Ecology
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• v.15 no.1
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• pp.1-7
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• 1992

The dvelopment of leaves in soybean(glycine max cv. yellow grain and glycine max cv. black grain)plant were assessed for the applicability of the plastochron index. The plastochron ages versus time in days were respectively 6.4 days in black grain and 9.6 days in yellow grain. Plots of plastochron indes(pi) versus time were linear, there were two distinct groups of regression line. when a point of intersection in the lines were upper than the reference length, pi was estimated n+(ln Ln-ln LR)/(ln Ln-ln Ln+2) or (n-1)+(ln Ln-1-ln LR)/(ln Ln-1-ln Ln+1) . When a point of intersection in the lines were less than the reference length, pl was estimated n+(ln Ln-ln LR)/(ln Ln-ln Ln+2) or (n-1)+(ln Ln-1-ln LR)/(ln Ln-1-ln Ln+1) . The growth rate of black grain plants analysed by pl was higher than yellow grain plants.

• Computers and Concrete
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• v.12 no.3
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• pp.243-259
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• 2013

Recent developments in Artificial Intelligence (AI) and computational intelligence have made it viable in the construction industry and structural analysis. This study usesthe Adaptive Network-based Fuzzy Inference System (ANFIS) as a modelling tool to predict the strain in tie section for High Strength Self Compacting Concrete (HSSCC) deep beams. 3773 experimental data were collected. The input data andits corresponding strains in tie section as output data were recorded at all loading stages. Results from ANFIS are compared with the classical linear regression (LR). The comparison shows that the ANFIS's results are highly accurate, precise and satisfactory.

• Geomechanics and Engineering
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• v.36 no.4
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• pp.381-390
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• 2024

The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R² value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money.

• The Korean Journal of Ecology
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• v.8 no.1
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• pp.1-5
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• 1985

The plastochron index (PI) provides possibility on studies of the effects of various environmental factors on morphological and physiological development of plants. The PI of Erickson and Michemlini(1957) could be used merely when leaf n is longer an leaf n+1 is smaller than the references length at any time. If both the lengths of leaf n and n+1 are smaller or longer than the reference length, it could not estimated. In this study, the PI of Erickson and Michelini was complemented and the linear patterns according to leaf arrangement was represented, PI is n-(lnLR-lnLn)/(lnLn-lnLn+1). And when both the lengths of leaf n and n+1 are longer than the length of reference, PI is n+1+(ln Ln+1-lnLR)/(lnLn-lnLn+1). The linear model of PI is changed by the various environmental factors and the linear patterns are different according to leaf arrangement. According to leaf arrangement, the equation of the general regression lines is Yin-(i-j)=a-(n-1)(q1+...+qi-1)-(q1+...+qj-1)+rt+$\varepsilon$. Where Y:the logarithmic of the leaf length in question, i:leaf number hang on the one node, n:the number counting from base, q:spacing on the Y-axis, j:0, 1, 2, ..., r:slope, t:time, $\varepsilon$:error.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.