Variance Analysis for Least ℓp-Norm Estimator in Mixture of Generalized Gaussian Noise
Summary :
Variance analysis is an important research topic to assess the quality of estimators. In this paper, we analyze the performance of the least ℓp-norm estimator in the presence of mixture of generalized Gaussian (MGG) noise. In the case of known density parameters, the variance expression of the ℓp-norm minimizer is first derived, for the general complex-valued signal model. Since the formula is a function of p, the optimal value of p corresponding to the minimum variance is then investigated. Simulation results show the correctness of our study and the near-optimality of the ℓp-norm minimizer compared with Cramér-Rao lower bound.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.5 pp.1226-1230
- Publication Date
- 2017/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.1226
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Yuan CHEN
University of Science & Technology Beijing
Long-Ting HUANG
Wuhan University of Technology
Xiao Long YANG
University of Science & Technology Beijing
Hing Cheung SO
City University of Hong Kong
Keyword
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Yuan CHEN, Long-Ting HUANG, Xiao Long YANG, Hing Cheung SO, "Variance Analysis for Least ℓp-Norm Estimator in Mixture of Generalized Gaussian Noise" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 5, pp. 1226-1230, May 2017, doi: 10.1587/transfun.E100.A.1226.
Abstract: Variance analysis is an important research topic to assess the quality of estimators. In this paper, we analyze the performance of the least ℓp-norm estimator in the presence of mixture of generalized Gaussian (MGG) noise. In the case of known density parameters, the variance expression of the ℓp-norm minimizer is first derived, for the general complex-valued signal model. Since the formula is a function of p, the optimal value of p corresponding to the minimum variance is then investigated. Simulation results show the correctness of our study and the near-optimality of the ℓp-norm minimizer compared with Cramér-Rao lower bound.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.1226/_p
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@ARTICLE{e100-a_5_1226,
author={Yuan CHEN, Long-Ting HUANG, Xiao Long YANG, Hing Cheung SO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Variance Analysis for Least ℓp-Norm Estimator in Mixture of Generalized Gaussian Noise},
year={2017},
volume={E100-A},
number={5},
pages={1226-1230},
abstract={Variance analysis is an important research topic to assess the quality of estimators. In this paper, we analyze the performance of the least ℓp-norm estimator in the presence of mixture of generalized Gaussian (MGG) noise. In the case of known density parameters, the variance expression of the ℓp-norm minimizer is first derived, for the general complex-valued signal model. Since the formula is a function of p, the optimal value of p corresponding to the minimum variance is then investigated. Simulation results show the correctness of our study and the near-optimality of the ℓp-norm minimizer compared with Cramér-Rao lower bound.},
keywords={},
doi={10.1587/transfun.E100.A.1226},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Variance Analysis for Least ℓp-Norm Estimator in Mixture of Generalized Gaussian Noise
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1226
EP - 1230
AU - Yuan CHEN
AU - Long-Ting HUANG
AU - Xiao Long YANG
AU - Hing Cheung SO
PY - 2017
DO - 10.1587/transfun.E100.A.1226
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2017
AB - Variance analysis is an important research topic to assess the quality of estimators. In this paper, we analyze the performance of the least ℓp-norm estimator in the presence of mixture of generalized Gaussian (MGG) noise. In the case of known density parameters, the variance expression of the ℓp-norm minimizer is first derived, for the general complex-valued signal model. Since the formula is a function of p, the optimal value of p corresponding to the minimum variance is then investigated. Simulation results show the correctness of our study and the near-optimality of the ℓp-norm minimizer compared with Cramér-Rao lower bound.
ER -
English
