• 제목/요약/키워드: Function Estimation

검색결과 3,052건 처리시간 0.026초

Robustizing Kalman filters with the M-estimating functions

  • Pak, Ro Jin
    • Communications for Statistical Applications and Methods
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    • 제25권1호
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    • pp.99-107
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    • 2018
  • This article considers a robust Kalman filter from the M-estimation point of view. Pak (Journal of the Korean Statistical Society, 27, 507-514, 1998) proposed a particular M-estimating function which has the data-based shaping constants. The Kalman filter with the proposed M-estimating function is considered. The structure and the estimating algorithm of the Kalman filter accompanying the M-estimating function are mentioned. Kalman filter estimates by the proposed M-estimating function are shown to be well behaved even when data are contaminated.

Bayesian and maximum likelihood estimation of entropy of the inverse Weibull distribution under generalized type I progressive hybrid censoring

  • Lee, Kyeongjun
    • Communications for Statistical Applications and Methods
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    • 제27권4호
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    • pp.469-486
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    • 2020
  • Entropy is an important term in statistical mechanics that was originally defined in the second law of thermodynamics. In this paper, we consider the maximum likelihood estimation (MLE), maximum product spacings estimation (MPSE) and Bayesian estimation of the entropy of an inverse Weibull distribution (InW) under a generalized type I progressive hybrid censoring scheme (GePH). The MLE and MPSE of the entropy cannot be obtained in closed form; therefore, we propose using the Newton-Raphson algorithm to solve it. Further, the Bayesian estimators for the entropy of InW based on squared error loss function (SqL), precautionary loss function (PrL), general entropy loss function (GeL) and linex loss function (LiL) are derived. In addition, we derive the Lindley's approximate method (LiA) of the Bayesian estimates. Monte Carlo simulations are conducted to compare the results among MLE, MPSE, and Bayesian estimators. A real data set based on the GePH is also analyzed for illustrative purposes.

공동주택 건물 외부공간 및 옥외시설의 공종별 수선비용 산정모델 (Repair Cost Estimation Model of the Building Exterior and Outdoor Facilities in Apartment Housing)

  • 이강희;채창우
    • KIEAE Journal
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    • 제16권3호
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    • pp.129-135
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    • 2016
  • Purpose: Building figuration is imperative to perceive the its value, environmental clean status and form. Therefore, maintenance activities of the building exterior are required to keep the housing condition and value. Each household should pay the repair cost which is brought out in the future. For this repair cost, the estimation model would needed to forecast and provide the required cost. This study aimed at providing the estimation model of the repair cost, using the repair survey data between the 2011 and 2014 in Seoul. Method: For these, it took various estimation function of repair cost such as 1st function, inverse function and so on. These above functions would be applied into the building exterior and outdoor facilities which figure the building shape and characteristics. Result: Results of this study are shown ; First, among 11 estimation models, the power function has a better statistics and goodness-of-fit than any other models. Second, the estimation model with a variable of household has a pattern in upward to the right. On the contrary, the model with management area is little downward to the right. Both of them are depended on the estimated parameter of the power function and the parameter smaller than 1.

ESTIMATION OF SCALE PARAMETER AND P(Y < X) FROM RAYLEIGH DISTRIBUTION

  • Kim, Chan-Soo;Chung, Youn-Shik
    • Journal of the Korean Statistical Society
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    • 제32권3호
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    • pp.289-298
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    • 2003
  • We consider the estimation problem for the scale parameter of the Rayleigh distribution using weighted balanced loss function (WBLF) which reflects both goodness of fit and precision. Under WBLF, we obtain the optimal estimator which creates a kind of balance between Bayesian and non-Bayesian estimation. We also deal with the estimation of R = P(Y < X) when Y and X are two independent but not identically distributed Rayleigh distribution under squared error loss function.

섭동/상관관계 기반 최적화 기법 (Perturbation/Correlation based Optimization)

  • 이수용
    • 제어로봇시스템학회논문지
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    • 제17권9호
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    • pp.875-881
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    • 2011
  • This paper describes a new method of estimating the gradient of a function with perturbation and correlation. We impose a known periodic perturbation to the input variable and observe the output of the function in order to obtain much richer and more reliable information. By taking the correlation between the input perturbation and the resultant function outputs, we can determine the gradient of the function. The computation of the correlation does not require derivatives; therefore the gradient can be estimated reliably. Robust estimation of the gradient using perturbation/correlation, which is very effective when an analytical solution is not available, is described. To verify the effectiveness of perturbation/correlation based estimation, the results of gradient estimation are compared with the analytical solutions of an example function. The effects of amplitude of the perturbation and number of samplings in a period are investigated. A minimization of a function with the gradient estimation method is performed.

Variance function estimation with LS-SVM for replicated data

  • Shim, Joo-Yong;Park, Hye-Jung;Seok, Kyung-Ha
    • Journal of the Korean Data and Information Science Society
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    • 제20권5호
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    • pp.925-931
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    • 2009
  • In this paper we propose a variance function estimation method for replicated data based on averages of squared residuals obtained from estimated mean function by the least squares support vector machine. Newton-Raphson method is used to obtain associated parameter vector for the variance function estimation. Furthermore, the cross validation functions are introduced to select the hyper-parameters which affect the performance of the proposed estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

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Reliability estimation and ratio distribution in a general exponential distribution

  • Lee, Chang-Soo;Moon, Yeung-Gil
    • Journal of the Korean Data and Information Science Society
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    • 제25권3호
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    • pp.623-632
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    • 2014
  • We shall consider the estimation for the parameter and the right tail probability in a general exponential distribution. We also shall consider the estimation of the reliability P(X < Y ) and the skewness trends of the density function of the ratio X=(X+Y) for two independent general exponential variables each having different shape parameters and known scale parameter. We then shall consider the estimation of the failure rate average and the hazard function for a general exponential variable having the density function with the unknown shape and known scale parameters, and for a bivariate density induced by the general exponential density.

Gompertz 소프트웨어 비용 추정 모델 (A Gompertz Model for Software Cost Estimation)

  • 이상운
    • 정보처리학회논문지D
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    • 제15D권2호
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    • pp.207-212
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    • 2008
  • 본 논문은 소프트웨어 비용추정 모델의 적합성을 평가하고, 가장 적합한 모델을 제시하였다. 먼저, 해당 모델의 함수를 변수변환시켜 선형식으로 만든다. 다음으로 실제 개발 소프트웨어의 비용 데이터가 모델의 선형식에 얼마나 적합한지로 모델의 성능을 평가한다. 모델 성능평가에는 절대오차 대신 상대오차 개념인 MMRE를 적용하였다. 기존의 소프트웨어 비용추정 모델은 Weibull, Gamma와 Rayleigh 함수를 따르고 있다. 본 논문에서는 성장곡선의 일종인 Gompertz 곡선 모델을 제안하였다. 추가로 다른 성장곡선들도 적합성을 검증하였다. 모델 성능평가 결과 Gompertz 성장곡선이 소프트웨어 비용추정 모델로 가장 적합한 성능을 보였다.

An optimal regularization for structural parameter estimation from modal response

  • Pothisiri, Thanyawat
    • Structural Engineering and Mechanics
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    • 제22권4호
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    • pp.401-418
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    • 2006
  • Solutions to the problems of structural parameter estimation from modal response using leastsquares minimization of force or displacement residuals are generally sensitive to noise in the response measurements. The sensitivity of the parameter estimates is governed by the physical characteristics of the structure and certain features of the noisy measurements. It has been shown that the regularization method can be used to reduce effects of the measurement noise on the estimation error through adding a regularization function to the parameter estimation objective function. In this paper, we adopt the regularization function as the Euclidean norm of the difference between the values of the currently estimated parameters and the a priori parameter estimates. The effect of the regularization function on the outcome of parameter estimation is determined by a regularization factor. Based on a singular value decomposition of the sensitivity matrix of the structural response, it is shown that the optimal regularization factor is obtained by using the maximum singular value of the sensitivity matrix. This selection exhibits the condition where the effect of the a priori estimates on the solutions to the parameter estimation problem is minimal. The performance of the proposed algorithm is investigated in comparison with certain algorithms selected from the literature by using a numerical example.