CWCU LMMSE Estimation Under Linear Model Assumptions

Oliver Lang; Mario Huemer

doi:10.1007/978-3-319-27340-2_67

CWCU LMMSE Estimation Under Linear Model Assumptions

Oliver Lang, Mario Huemer

Institute of Signal Processing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

The classical unbiasedness condition utilized e.g. by the best linear unbiased estimator (BLUE) is very stringent. By softening the “global” unbiasedness condition and introducing component-wise conditional unbiasedness conditions instead, the number of constraints limiting the estimator’s performance can in many cases significantly be reduced. In this paper we extend the findings on the component-wise conditionally unbiased linear minimum mean square error (CWCU LMMSE) estimator under linear model assumptions. We discuss the CWCU LMMSE estimator for complex proper Gaussian parameter vectors, and for mutually independent (and otherwise arbitrarily distributed) parameters. Finally, the beneficial properties of the CWCU LMMSE estimator are demonstrated in two applications.

Original language	English
Title of host publication	Lecture Notes in Computer Science (LNCS): Computer Aided Systems Theory - EUROCAST 2015
Publisher	Springer International Publishing
Pages	537-545
Number of pages	9
Volume	9520
ISBN (Print)	978-3-319-27339-6
DOIs	https://doi.org/10.1007/978-3-319-27340-2_67
Publication status	Published - Dec 2015

Fields of science

202040 Transmission technology
202 Electrical Engineering, Electronics, Information Engineering
202037 Signal processing

JKU Focus areas

Computation in Informatics and Mathematics
Mechatronics and Information Processing

Access to Document

10.1007/978-3-319-27340-2_67

Knowledge-Aided Methods in Estimation Theory
Lang, O. (Researcher) & Huemer, M. (PI)
01.03.2014 → 28.02.2018
Project: Other › Project from scientific scope of research unit

Cite this

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title = "CWCU LMMSE Estimation Under Linear Model Assumptions",

abstract = "The classical unbiasedness condition utilized e.g. by the best linear unbiased estimator (BLUE) is very stringent. By softening the “global” unbiasedness condition and introducing component-wise conditional unbiasedness conditions instead, the number of constraints limiting the estimator{\textquoteright}s performance can in many cases significantly be reduced. In this paper we extend the findings on the component-wise conditionally unbiased linear minimum mean square error (CWCU LMMSE) estimator under linear model assumptions. We discuss the CWCU LMMSE estimator for complex proper Gaussian parameter vectors, and for mutually independent (and otherwise arbitrarily distributed) parameters. Finally, the beneficial properties of the CWCU LMMSE estimator are demonstrated in two applications.",

author = "Oliver Lang and Mario Huemer",

year = "2015",

month = dec,

doi = "10.1007/978-3-319-27340-2_67",

language = "English",

isbn = "978-3-319-27339-6",

volume = "9520",

pages = "537--545",

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publisher = "Springer International Publishing",

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T1 - CWCU LMMSE Estimation Under Linear Model Assumptions

AU - Lang, Oliver

AU - Huemer, Mario

PY - 2015/12

Y1 - 2015/12

N2 - The classical unbiasedness condition utilized e.g. by the best linear unbiased estimator (BLUE) is very stringent. By softening the “global” unbiasedness condition and introducing component-wise conditional unbiasedness conditions instead, the number of constraints limiting the estimator’s performance can in many cases significantly be reduced. In this paper we extend the findings on the component-wise conditionally unbiased linear minimum mean square error (CWCU LMMSE) estimator under linear model assumptions. We discuss the CWCU LMMSE estimator for complex proper Gaussian parameter vectors, and for mutually independent (and otherwise arbitrarily distributed) parameters. Finally, the beneficial properties of the CWCU LMMSE estimator are demonstrated in two applications.

AB - The classical unbiasedness condition utilized e.g. by the best linear unbiased estimator (BLUE) is very stringent. By softening the “global” unbiasedness condition and introducing component-wise conditional unbiasedness conditions instead, the number of constraints limiting the estimator’s performance can in many cases significantly be reduced. In this paper we extend the findings on the component-wise conditionally unbiased linear minimum mean square error (CWCU LMMSE) estimator under linear model assumptions. We discuss the CWCU LMMSE estimator for complex proper Gaussian parameter vectors, and for mutually independent (and otherwise arbitrarily distributed) parameters. Finally, the beneficial properties of the CWCU LMMSE estimator are demonstrated in two applications.

U2 - 10.1007/978-3-319-27340-2_67

DO - 10.1007/978-3-319-27340-2_67

M3 - Conference proceedings

SN - 978-3-319-27339-6

VL - 9520

SP - 537

EP - 545

BT - Lecture Notes in Computer Science (LNCS): Computer Aided Systems Theory - EUROCAST 2015

PB - Springer International Publishing

ER -

CWCU LMMSE Estimation Under Linear Model Assumptions

Abstract

Fields of science

JKU Focus areas

Access to Document

Projects

Knowledge-Aided Methods in Estimation Theory

Cite this