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Giummolè, Federica and Ventura, Laura (2001) Improved maximum likelihood estimation in heteroscedastic nonlinear regression models. [Working Paper] WORKING PAPER SERIES, 15/2001 . , PADOVA (Inedito)

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Abstract (english)

Nonlinear heteroscedastic regression models are a widely used class of models in applied statistics, with applications especially in biology, medicine or chemistry. Nonlinearity and variance heterogeneity can make likelihood estimation for a scalar parameter of interest rather inaccurate for small or moderate samples. In this paper, we suggest a new approach to point estimation based on estimating equations obtained from higher-order pivots for the parameter of interest. In particular, we take as an estimating function the modified directed likelihood. This is a higher-order pivotal quantity that can be easily computed in practice for nonlinear heteroscedastic models with normally distributed errors , using a recently developed S-PLUS library (HOA, 2000) . The estimators obtained from this procedure are a refinement of the maximum likelihood estimators, improving their small sample properties and keeping equivariance under reparameterisation. Two applications to real data sets are discussed.

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EPrint type:Working Paper
Anno di Pubblicazione:October 2001
Key Words:Likelihood asymptotics; Modified directed likelihood; Nonlinear heteroscedastic models; Parameterisation equivariance.
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:7364
Depositato il:26 Nov 2014 12:19
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