Parameter estimation for linear regression models in powerline communication systems noise using Generalized Method of Moments (GMM)

M. Mosalaosi, T. J.O. Afullo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2016 Progress In Electromagnetics Research Symposium, PIERS 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4858-4862
Number of pages5
ISBN (Electronic)9781509060931
DOIs
Publication statusPublished - Nov 3 2016
Externally publishedYes
Event2016 Progress In Electromagnetics Research Symposium, PIERS 2016 - Shanghai, China
Duration: Aug 8 2016Aug 11 2016

Publication series

Name2016 Progress In Electromagnetics Research Symposium, PIERS 2016 - Proceedings

Conference

Conference2016 Progress In Electromagnetics Research Symposium, PIERS 2016
CountryChina
CityShanghai
Period8/8/168/11/16

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Radiation
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

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