Estimation of Radioimmunoassay Data Using Robust Nonlinear Regression Methods

Autor:	H.-P. Altenburg
Rok vydání:	1992
Předmět:	Winsorized mean Nonlinear system Overdispersion Estimation theory Norm (mathematics) Statistics Linear regression Outlier Statistics::Methodology Nonlinear regression Mathematics
Zdroj:	Computational Statistics ISBN: 9783662268131
DOI:	10.1007/978-3-662-26811-7_51
Popis:	The paper discusses several nonlinear regression methods estimating contaminated radioimmunoassay data. The underlying model is an overdispersed Poisson process with four regression line parameters and one parameter related to the overdispersion of the variance. A generalized least-squares (GLS) algorithm can be used for parameter estimation of noncontaminated data. In the presence of outliers different methods are discussed such as L p -norm or nonlinear generalizations of Huber’s M-estimator. The best estimation results we get by a winsorized version of the GLS algorithm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::89a9cfacdbfb07fa7cb8f0f646e38fbf https://doi.org/10.1007/978-3-662-26811-7_51 Zobrazit plný text záznamu