• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.255-264
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• 2008

It is common in practice to use mean squared error(MSE) for prediction. Recently, Park and Shin (2005) and Jones et al. (2007) studied prediction based on mean squared relative error(MSRE). We proposed a new nonparametric way of prediction based on MSRE substituting Jones et al. (2007) and provided a small simulation study which highly supports the proposed method.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.561-568
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• 2015

In this study we predict apartment prices per unit in Daegu-Gyeongbuk areas by spatial lag and spatial error models, both of which belong to so-called spatial regression model. A spatial weight matrix is constructed by k-nearest neighbours method and then the models for the apartment prices in March, 2012 are fitted using the weight matrix. The apartment prices in March, 2013 are predicted by the fitted spatial regression models and then performances of two spatial regression models are compared by RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error).

• Journal of the Korean Statistical Society
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• v.33 no.3
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• pp.323-337
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• 2004

This article coined a general class of estimators for various measures in normal population when some' a priori' or guessed value of standard deviation a is available in addition to sample information. The class of estimators is primarily defined for a function of standard deviation. An unbiased estimator and the minimum mean squared error estimator are worked out and the suggested class of estimators is compared with these classical estimators. Numerical computations in terms of percent relative efficiency and absolute relative bias established the merits of the proposed class of estimators especially for small samples. Simulation study confirms the excellence of the proposed class of estimators. The beauty of this article lies in estimation of various measures like standard deviation, variance, Fisher information, precision of sample mean, process capability index $C_{p}$, fourth moment about mean, mean deviation about mean etc. as particular cases of the proposed class of estimators.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1B
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• pp.21-27
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• 2011

When execute runoff forecasting, can not remove perfectly uncertainty of forecasting results. But, reduce uncertainty by various techniques analysis. This study applied various forecasting techniques for runoff prediction's accuracy elevation in Gongju basin. statics techniques is ESP, Period Average & Moving average, Exponential Smoothing, Winters, Auto regressive moving average process. Authoritativeness estimation with results of runoff forecasting by each techniques used MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), RRMSE (Relative Root Mean Squared Error), Mean Absolute Percentage Error (MAPE), TIC (Theil Inequality Coefficient). Result that use MAE, RMSE, RRMSE, MAPE, TIC and confirm improvement effect of runoff forecasting, ESP techniques than the others displayed the best result.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.445-453
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• 2011

Almost all small area estimations are obtained by minimizing the mean squared error. Recently relative error prediction methods have been developed and adapted to small area estimation. Usually the estimators obtained by using relative error prediction is called a shrinkage estimator. Especially when data set consists of large range values, the shrinkage estimator is known as having good statistical properties and an easy interpretation. In this paper we study the shrinkage estimators based on logistic regression type estimators for small area estimation. Some simulation studies are performed and the Economically Active Population Survey data of 2005 is used for comparison.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.367-374
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• 1999

Biological specimens contain several components and multilinear models are very useful in analyzing these data. After fitting the model the number of components are determined by the change of mean squared error however this method is quite rule of thumb. in this paper we suggest a measure to decide the number of components based on the relative change of to mean squared error. Simulations are done and applications to real data set are given as illustrations.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.351-368
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• 2021

For a probabilistic model with positively skewed data, a lognormal distribution is one of the key distributions that play a critical role. Several lognormal models can be found in various areas, such as medical science, engineering, and finance. In this paper, we propose a new estimator for a lognormal mean and depict the performance of the proposed estimator in terms of the relative mean squared error (RMSE) compared with Shen's estimator (Shen et al., 2006), which is considered the best estimator among the existing methods. The proposed estimator includes a tuning parameter. By finding the optimal value of the tuning parameter, we can improve the average performance of the proposed estimator over the typical range of σ². The bias reduction of the proposed estimator tends to exceed the increased variance, and it results in a smaller RMSE than Shen's estimator. A numerical study reveals that the proposed estimator has performance comparable with Shen's estimator when σ² is small and exhibits a meaningful decrease in the RMSE under moderate and large σ² values.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.231-238
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• 1998

A two stage shrinkage testimator for the mean of an exponential distribution is considered with the assumption that an initial estimate of the mean is available. Mean squared error(MSE) of testimator and its relative efficiency (to usual single sample mean) are briefly reviewed. It is shown that relative efficiency depends only on the ratio of true mean value and its initial estimate.

• Advances in Computational Design
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• v.7 no.3
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.915-918
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• 2001

In this paper we study a new convergence behavior of the least mean fourth (LMF) algorithm where the error raised to the power of four is minimized for a multiple sinusoidal input and Gaussian measurement noise. Here we newly obtain the convergence equation for the sum of the mean of the squared weight errors, which indicates that the transient behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant. It should be noted that no similar results can be expected from the previous analysis by Walach and Widrow.