{"title":"Small sample properties of the RSS estimation algorithm for Gaussian measurement noise","authors":"C. S. Agate, R. Iltis","doi":"10.1109/ACSSC.1997.679183","DOIUrl":null,"url":null,"abstract":"The statistics of the reduced sufficient statistics (RSS) estimator are derived for the nonlinear additive white Gaussian noise measurement model. The RSS algorithm recursively propagates a set of sufficient statistics for a mixture density which approximates the true posterior density of a parameter vector. The joint probability density function for the weighting coefficients of the mixture density is derived for the case of additive white Gaussian noise. Through integration of this density, the estimator bias and mean-squared error are determined. The results are applied to a scalar estimation problem in which the sample-averaged statistics are compared to those derived from numerical integration of the density function.","PeriodicalId":240431,"journal":{"name":"Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.1997.679183","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
The statistics of the reduced sufficient statistics (RSS) estimator are derived for the nonlinear additive white Gaussian noise measurement model. The RSS algorithm recursively propagates a set of sufficient statistics for a mixture density which approximates the true posterior density of a parameter vector. The joint probability density function for the weighting coefficients of the mixture density is derived for the case of additive white Gaussian noise. Through integration of this density, the estimator bias and mean-squared error are determined. The results are applied to a scalar estimation problem in which the sample-averaged statistics are compared to those derived from numerical integration of the density function.
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