• Title, Summary, Keyword: mean square error

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An Error Assessment of the Kriging Based Approximation Model Using a Mean Square Error (평균제곱오차를 이용한 크리깅 근사모델의 오차 평가)

  • Ju Byeong-Hyeon;Cho Tae-Min;Jung Do-Hyun;Lee Byung-Chai
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.8
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    • pp.923-930
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    • 2006
  • A Kriging model is a sort of approximation model and used as a deterministic model of a computationally expensive analysis or simulation. Although it has various advantages, it is difficult to assess the accuracy of the approximated model. It is generally known that a mean square error (MSE) obtained from the kriging model can't calculate statistically exact error bounds contrary to a response surface method, and a cross validation is mainly used. But the cross validation also has many uncertainties. Moreover, the cross validation can't be used when a maximum error is required in the given region. For solving this problem, we first proposed a modified mean square error which can consider relative errors. Using the modified mean square error, we developed the strategy of adding a new sample to the place that the MSE has the maximum when the MSE is used for the assessment of the kriging model. Finally, we offer guidelines for the use of the MSE which is obtained from the kriging model. Four test problems show that the proposed strategy is a proper method which can assess the accuracy of the kriging model. Based on the results of four test problems, a convergence coefficient of 0.01 is recommended for an exact function approximation.

Comparison of Statistic Methods for Evaluating Crop Model Performance (작물모형 평가를 위한 통계적 방법들에 대한 비교)

  • Kim, Junhwan;Lee, Chung-Kuen;Shon, Jiyoung;Choi, Kyung-Jin;Yoon, Younghwan
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.14 no.4
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    • pp.269-276
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    • 2012
  • The objective of this short communication is to introduce several evaluation methods to crop model users because the evaluation of crop model performance is an important step to develop or select crop model. In this paper, mean error, mean absolute error, index of agreement, root mean square error, efficiency of model, accuracy factor and bias factor were explained and compared in terms of dimension and observed number. Efficiency of model and index of agreement are dimensionless and independent of number of observation. Relative root mean square, accuracy factor and bias factor are dimensionless and not independent of number of observation. Mean error and mean absolute error are affected by dimension and number of observation.

Shrinkage Prediction for Small Area Estimations (축소예측을 이용한 소지역 추정)

  • Hwang, Hee-Jin;Shin, Key-Il
    • The Korean Journal of Applied Statistics
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    • v.21 no.1
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    • pp.109-123
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    • 2008
  • Many small area estimation methods have been suggested. Also for the comparison of the estimation methods, model diagnostic checking techniques have been studied. Almost all of the small area estimators were developed by minimizing MSE(Mean square error) and so the MSE is the well-known comparison criterion for superiority. In this paper we suggested a new small area estimator based on minimizing MSPE(Mean square percentage error) which is recently re-highlighted. Also we compared the new suggested estimator with the estimators explained in Shin et al. (2007) using MSE, MSPE and other diagnostic checking criteria.

Adjustable Multiple Relay Selection Based on Steady-State Mean Square Joint Error for Cooperative Communication

  • Liu, Zhiyong;Zhang, Qinyu;Mu, Liwei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.9
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    • pp.4326-4341
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    • 2016
  • In this paper, an adjustable multiple relay selection (MRS) scheme for cooperative communication with amplify-and-forward (AF) relay under frequency selective channels is proposed. In the proposed scheme, the relays are ordered firstly by the steady-state mean square error (MSE), then the relays are sequentially selected out from N relays and the number of cooperating relays is adjusted dynamically according to the steady-state mean square joint error (MSJE). The aim of this work is to dynamically estimate the optimum number No of cooperating relays. Optimum means the minimum number of cooperating relays, No, achieving the minimum level of steady-state MSJE. Numerical results verify the analyses and show that the scheme can adaptively adjust the number of cooperating relays, and outperform conventional relay selection schemes. Hence, the proposed scheme provides better tradeoff between BER performance and spectral efficiency and to save more energy in cooperative wireless networks.

Adaptive Feedback Interference Cancellation Algorithm Using Correlations for Adaptive Interference Cancellation System (적응 간섭 제거 시스템을 위한 상관도를 적용한 적응적 궤환 간섭 제거 알고리즘)

  • Han, Yong-Sik;Yang, Woon-Geun
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.21 no.4
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    • pp.427-432
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    • 2010
  • To reduce the outage probability and to increase the transmission capacity, the importance of repeaters in cellular systems is increasing. But a RF(Radio Frequency) repeater has a problem that the output of the transmit antenna is partially feedback to the receive antenna, which is feedback interference. In this paper, we proposed adaptive Sign-Sign LMS(Least Mean Square) algorithm using correlations for the performance enhancement of RF repeater. The weight vector is updated by using sign of input signal and error signal to the least squared error of the conventional algorithms. When compared with the conventional method, the proposed canceller achieves the maximum 10 dB performance gain in terms of the MSE(Mean Square Error).

A Study on Adaptive Interference Canceller of Wireless Repeater for Wideband Code Division Multiple Access System (WCDMA시스템 무선 중계기의 적응간섭제거기에 관한 연구)

  • Han, Yong-Sik;Yang, Woon-Geun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.7
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    • pp.1321-1327
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    • 2009
  • In this paper, as the mobile communication service is widely used and the demand for wireless repeaters is rapidly increasing because of the easiness of extending service areas. But a wireless repeater has a problem the oscillation due to feedback signal. We proposed a new hybrid interference canceller using the adaptive filter with CMA(Constant Modulus Algorithm)-Grouped LMS(Least Mean Square) algorithm in the adaptive interference canceller. The proposed interference canceller has better channel adaptive performance and a lower MSE(Mean Square Error) than conventional structure because it uses the cancellation method of Grouped LMS algorithm. The proposed detector uses the LMS algorithms with two different step size to reduce mean square error and to obtain fast convergence. This structure reduces the number of iterations for the same MSE performance and hardware complexity compared to conventional nonlinear interference canceller.

Prediction of the interest spread using VAR model (벡터자기회귀모형에 의한 금리스프레드의 예측)

  • Kim, Junhong;Jin, Dalae;Lee, Jisun;Kim, Suji;Son, Young Sook
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.6
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    • pp.1093-1102
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    • 2012
  • In this paper, we predicted the interest spread using the VAR (vector autoregressive) model. Variables used in the VAR model were selected among 56 domestic and foreign macroeconomic time series through crosscorrelation and Granger causality test. The performance of the VAR model was compared with the univariate time series model, AR (autoregressive) model, in view of MAPE (mean absolute percentage error) and RMSE (root mean square error) of forecasts for the last twelve months.

Approximate Variance of Least Square Estimators for Regression Coefficient under Inclusion Probability Proportional to Size Sampling (포함확률비례추출에서 회귀계수 최소제곱추정량의 근사분산)

  • Kim, Kyu-Seong
    • Communications for Statistical Applications and Methods
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    • v.19 no.1
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    • pp.23-32
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    • 2012
  • This paper deals with the bias and variance of regression coefficient estimators in a finite population. We derive approximate formulas for the bias, variance and mean square error of two estimators when we select a fixed-size inclusion probability proportional to the size sample and then estimate regression coefficients by the ordinary least square estimator as well as the weighted least square estimator based on the selected sample data. Necessary and sufficient conditions for the comparison of the two estimators in terms of variance and mean square error are suggested. In addition, a simple example is introduced to numerically compare the variance and mean square error of the two estimators.

Design-Based Small Area Estimation for the Korean Economically Active Population Survey (시군구 실업자 총계 추정을 위한 설계기반 간접추정법)

  • 정연수;이계오;이우일
    • The Korean Journal of Applied Statistics
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    • v.16 no.1
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    • pp.1-14
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    • 2003
  • In this study, we suggest the method of small area estimation based on the Economically Active Population Survey (EAPS) data in producing unemployment statistics for the local self-government areas (LSGAs) within large areas. The small area estimators considered are design-based indirect estimators such as the synthetic and composite estimators. The jackknife mean square error was used as a measure of accuracy of such small area estimators. The total unemployed and jackknife mean square errors of the 10 LSGAs within the large area of ChoongBuk region are derived from the estimation procedure suggested in this study, using EAPS data of December 2000. The reliability of small area estimators was assessed using the relative bias values and relative root mean square errors of these estimators. We find that under the current Korean EAPS system, the composite estimator turns out to be much more stable than other estimators.

Signal Estimation Using Covariance Matrix of Mutual Coupling and Mean Square Error

  • Lee, Kwan-Hyeong
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.11 no.6
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    • pp.691-696
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    • 2018
  • We propose an algorithm to update weight to use the mean square error method and mutual coupling matrix in a coherent channel. The algorithm proposed in this paper estimates the desired signal by using the updated weight. The updated weight is obtained by covariance matrix using mean square error and mutual coupling matrix. The MUSIC algorithm, which is direction of arrival estimation method, is mostly used in the desired signal estimation. The MUSIC algorithm has a good resolution because it uses subspace techniques. The proposed method estimates the desired signal by updating the weights using the mutual coupling matrix and mean square error method. Through simulation, we analyze the performance by comparing the classical MUSIC and the proposed algorithm in a coherent channel. In this case of the coherent channel for estimating at the three targets (-10o, 0o, 10o), the proposed algorithm estimates all the three targets (-10o, 0o, 10o). But the classical MUSIC algorithm estimates only one target (x, x, 10o). The simulation results indicate that the proposed method is superior to the classical MUSIC algorithm for desired signal estimation.