The performance comparison of optimally weighted LS and linear minimum variance estimation for linear model with random input

Yunmin Zhu; Juan Zhao; X. Rong Li

The performance comparison of optimally weighted LS and linear minimum variance estimation for linear model with random input

Yunmin Zhu^*, Juan Zhao, X. Rong Li

^*Corresponding author for this work

School of Information and Electronics

Research output: Contribution to journal › Conference article › peer-review

4 Citations (Scopus)

Abstract

The performance comparison of the optimally weighted LS estimate and the linear minimum variance estimate for a linear model with random input is presented. In this case optimally weighted LS estimate is not a linear estimate of a parameter given input and observation anymore while linear minimum variance estimate still is. Under a certain conditions on variance matrix invertibility, we show that the optimally weighted LS estimated outperforms the linear minimum variance estimates provided that they have the same a priori information on the parameter being estimated.

Original language	English
Pages (from-to)	4258-4263
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	4
Publication status	Published - 2002
Event	41st IEEE Conference on Decision and Control - Las Vegas, NV, United States Duration: 10 Dec 2002 → 13 Dec 2002

Cite this

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title = "The performance comparison of optimally weighted LS and linear minimum variance estimation for linear model with random input",

abstract = "The performance comparison of the optimally weighted LS estimate and the linear minimum variance estimate for a linear model with random input is presented. In this case optimally weighted LS estimate is not a linear estimate of a parameter given input and observation anymore while linear minimum variance estimate still is. Under a certain conditions on variance matrix invertibility, we show that the optimally weighted LS estimated outperforms the linear minimum variance estimates provided that they have the same a priori information on the parameter being estimated.",

author = "Yunmin Zhu and Juan Zhao and Li, {X. Rong}",

year = "2002",

language = "English",

volume = "4",

pages = "4258--4263",

journal = "Proceedings of the IEEE Conference on Decision and Control",

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note = "41st IEEE Conference on Decision and Control ; Conference date: 10-12-2002 Through 13-12-2002",

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AU - Zhu, Yunmin

AU - Zhao, Juan

AU - Li, X. Rong

PY - 2002

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N2 - The performance comparison of the optimally weighted LS estimate and the linear minimum variance estimate for a linear model with random input is presented. In this case optimally weighted LS estimate is not a linear estimate of a parameter given input and observation anymore while linear minimum variance estimate still is. Under a certain conditions on variance matrix invertibility, we show that the optimally weighted LS estimated outperforms the linear minimum variance estimates provided that they have the same a priori information on the parameter being estimated.

AB - The performance comparison of the optimally weighted LS estimate and the linear minimum variance estimate for a linear model with random input is presented. In this case optimally weighted LS estimate is not a linear estimate of a parameter given input and observation anymore while linear minimum variance estimate still is. Under a certain conditions on variance matrix invertibility, we show that the optimally weighted LS estimated outperforms the linear minimum variance estimates provided that they have the same a priori information on the parameter being estimated.

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M3 - Conference article

AN - SCOPUS:0036995362

SN - 0743-1546

VL - 4

SP - 4258

EP - 4263

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

T2 - 41st IEEE Conference on Decision and Control

Y2 - 10 December 2002 through 13 December 2002

ER -

The performance comparison of optimally weighted LS and linear minimum variance estimation for linear model with random input

Abstract

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