TY - GEN
T1 - Parameter estimation for linear regression models in powerline communication systems noise using Generalized Method of Moments (GMM)
AU - Mosalaosi, M.
AU - Afullo, T. J.O.
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/11/3
Y1 - 2016/11/3
N2 - Parameter estimation of linear regression models usually employs least squares (LS) and maximum likelihood (ML) estimators. While maximum likelihood remains one of the best estimators within the classical statistics paradigm to date, it is highly reliant on the assumption about the joint probability distribution of the data for optimal results. In this paper we use the Generalized Method of Moments (GMM) to address the deficiencies of LS/ML in order to estimate the underlying data generating process (DGP). We use GMM as a statistical technique that incorporate observed noise data with the information in population moment conditions to determine estimates of unknown parameters of the underlying model. Periodic impulsive noise (short-term) has been measured, deseasonalized and modeled using GMM. The numerical results show that the model captures the noise process accurately.
AB - Parameter estimation of linear regression models usually employs least squares (LS) and maximum likelihood (ML) estimators. While maximum likelihood remains one of the best estimators within the classical statistics paradigm to date, it is highly reliant on the assumption about the joint probability distribution of the data for optimal results. In this paper we use the Generalized Method of Moments (GMM) to address the deficiencies of LS/ML in order to estimate the underlying data generating process (DGP). We use GMM as a statistical technique that incorporate observed noise data with the information in population moment conditions to determine estimates of unknown parameters of the underlying model. Periodic impulsive noise (short-term) has been measured, deseasonalized and modeled using GMM. The numerical results show that the model captures the noise process accurately.
UR - http://www.scopus.com/inward/record.url?scp=85006789722&partnerID=8YFLogxK
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U2 - 10.1109/PIERS.2016.7735775
DO - 10.1109/PIERS.2016.7735775
M3 - Conference contribution
AN - SCOPUS:85006789722
T3 - 2016 Progress In Electromagnetics Research Symposium, PIERS 2016 - Proceedings
SP - 4858
EP - 4862
BT - 2016 Progress In Electromagnetics Research Symposium, PIERS 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 Progress In Electromagnetics Research Symposium, PIERS 2016
Y2 - 8 August 2016 through 11 August 2016
ER -