Minimum Variance Unbiased Estimation in the Gamma Distribution

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

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Minimum variance unbiased estimation of frequency offset in OFDM systems, a blind synchronization approach

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100) ◽

10.1109/icassp.2000.861151 ◽

2002 ◽

Cited By ~ 1

Author(s):

N. Lashkarian ◽

S. Kiaei

Keyword(s):

Frequency Offset ◽

Minimum Variance ◽

Unbiased Estimation ◽

Ofdm Systems

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The influence of SO4 and NO3 to the acidity (pH) of rainwater using minimum variance quadratic unbiased estimation (MIVQUE) and maximum likelihood methods

10.1063/1.4979455 ◽

2017 ◽

Author(s):

Shintia Ulfa Dilla ◽

Yudhie Andriyana ◽

Sudartianto

Keyword(s):

Maximum Likelihood ◽

Minimum Variance ◽

Unbiased Estimation ◽

Likelihood Methods ◽

Maximum Likelihood Methods

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Time-Varying Wind Load Identification Based on Minimum-Variance Unbiased Estimation

Shock and Vibration ◽

10.1155/2017/9301876 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Huili Xue ◽

Kun Lin ◽

Yin Luo ◽

Hongjun Liu

Keyword(s):

Damping Ratio ◽

Estimation Method ◽

Minimum Variance ◽

Wind Load ◽

Ar Model ◽

Unbiased Estimation ◽

Recursive Identification ◽

Time Varying ◽

Building Structures ◽

Shear Building

A minimum-variance unbiased estimation method is developed to identify the time-varying wind load from measured responses. The formula derivation of recursive identification equations is obtained in state space. The new approach can simultaneously estimate the entire wind load and the unknown structural responses only with limited measurement of structural acceleration response. The fluctuating wind speed process is investigated by the autoregressive (AR) model method in time series analysis. The accuracy and feasibility of the inverse approach are numerically investigated by identifying the wind load on a twenty-story shear building structure. The influences of the number and location of accelerometers are examined and discussed. In order to study the stability of the proposed method, the effects of the errors in crucial factors such as natural frequency and damping ratio are discussed through detailed parametric analysis. It can be found from the identification results that the proposed method can identify the wind load from limited measurement of acceleration responses with good accuracy and stability, indicating that it is an effective approach for estimating wind load on building structures.

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