Locally minimum variance unbiased estimator in a discontinuous density function

Metrika ◽  
1987 ◽  
Vol 34 (1) ◽  
pp. 1-15 ◽  
Author(s):  
M. Akahira ◽  
K. Takeuchi
Keyword(s):  
Density Function ◽  
Unbiased Estimator ◽  
Minimum Variance ◽  
Minimum Variance Unbiased Estimator

scholarly journals Active Control of Edgewise Vibrations in Wind Turbine Blades Using Stochastic Disturbance Accommodating Control

2017 ◽  
Author(s):  
Cong Cong
Keyword(s):  
Wind Turbine ◽  
Unbiased Estimator ◽  
Turbine Blades ◽  
Minimum Variance ◽  
Aerodynamic Load ◽  
Unknown Input ◽  
Linear Quadratic ◽  
Minimum Variance Unbiased Estimator ◽  
Stochastic Disturbance ◽  
Disturbance Accommodating Control

Vibrations of blades and tower have important impact for wind turbine. This paper presents a active controller design to suppress blade edgewise vibrations under aerodynamic load and gravitational load.Treating the sum of aerodynamic load input in edgewise direction and gravitational load as unknown disturbance input,a stochastic disturbance accommodating control(SDAC) approach is proposed to design a controller which it utilizes a minimum-variance unbiased estimator(MVUE) to estimate both state and unknown input. The stability analysis proved that the proposed SDAC is bounded in mean square.In order to verify the performance of the minimum-variance unbiased estimator and the proposed SDAC, numerical simulations using Matlab/Simulink have been carried out for the National Renewable Energy Laboratory 5-MW wind turbine.Under the different circumstance which exists the random process and measure noise and noise free. It is shown that the estimation value by MVUE can tracking the real state and unknown input. The results are also compared to the traditional linear quadratic regulator(LQR) and show that the proposed stochastic disturbance accommodating control scheme can further reduce displacement in edgewise vibrations direction and the control strategy is more effective than the LQR.

Determination of CRLB and Minimum Variance Unbiased Estimator of a DC Signal in AWGN Using Laplace Transform

2021 ◽  
Vol 40 (1) ◽  
pp. 79-86
Author(s):  
Abdi Abdalla
Keyword(s):  
Lower Bound ◽  
Gaussian Noise ◽  
Unbiased Estimator ◽  
Laplace Transformation ◽  
Additive White Gaussian Noise ◽  
Minimum Variance ◽  
Minimum Variance Unbiased Estimator ◽  
Mathematical Background ◽  
Alternative Approach

This paper presents an alternative approach for the determination of Cramer-Rao Lower Bound (CRLB) and Minimum Variance Unbiased Estimator (MVUE) using Laplace transformation. In this work, a DC signal in Additive White Gaussian Noise (AWGN) was considered. During the investigation, a number of experiments were conducted to analyze different possible outputs under different conditions, and then the patterns of the outcomes were studied. Finally closed-form expressions for the CRLB and MVUE were deduced employing the Laplace transformation. The resulting expressions show that the proposed method has almost the same number of steps as the existing method. However, the later requires only the knowledge of algebra to arrive at the CRLB expressions contrary to the existing approach where a strong mathematical background is required and hence making it superior over the existing method, in that sense.

The Improved Estimation of σ in Quality Control, Revisited

1997 ◽  
Vol 11 (1) ◽  
pp. 37-42
Author(s):  
John E. Angus
Keyword(s):  
Quality Control ◽  
Unbiased Estimator ◽  
Mean Squared Error ◽  
Minimum Variance ◽  
Underlying Distribution ◽  
Minimum Variance Unbiased Estimator ◽  
Usual Estimator ◽  
Squared Error ◽  
Standard Quality ◽  
The Mean

Recently, Derman and Ross (1995, An improved estimator of a in quality control, Probability in the Engineering and Informational Sciences 9: 411–415) derived an estimator of the standard deviation in the standard quality control model and showed that it had smaller mean squared error than the usual estimator. The new estimator was also shown to be consistent even when the underlying distribution deviates from normality, unlike the usual estimator. In this note, the mean squared error is further improved via shrinkage of the Derman-Ross estimator, and a consistent minimum variance unbiased estimator is presented. Finally, by making use of additional subgroup statistics, a minimum variance unbiased estimator is derived and further improved via shrinkage.

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