M-procedures in the general multivariate nonlinear regression model

Víctor Leiva; Antonio Sanhueza; Pranab K. Sen; Nelson Araneda

M-procedures in the general multivariate nonlinear regression model

Víctor Leiva, Antonio Sanhueza, Pranab K. Sen, Nelson Araneda

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

6 Citas (Scopus)

Resumen

In the multivariate nonlinear regression model, parameter estimators and test statistics based on least squares and maximum likelihood methods are usually nonrobust. For this type of models, we introduce M-estimators and M-tests, which are robust to departures from normality. In addition, we study the asymptotic properties and consider a computational algorithm for these estimators.

Idioma original	Inglés
Páginas (desde-hasta)	1-13
Número de páginas	13
Publicación	Pakistan Journal of Statistics
Volumen	26
N.º	1
Estado	Publicada - ene. 2010
Publicado de forma externa	Sí

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title = "M-procedures in the general multivariate nonlinear regression model",

abstract = "In the multivariate nonlinear regression model, parameter estimators and test statistics based on least squares and maximum likelihood methods are usually nonrobust. For this type of models, we introduce M-estimators and M-tests, which are robust to departures from normality. In addition, we study the asymptotic properties and consider a computational algorithm for these estimators.",

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AU - Sen, Pranab K.

AU - Araneda, Nelson

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AB - In the multivariate nonlinear regression model, parameter estimators and test statistics based on least squares and maximum likelihood methods are usually nonrobust. For this type of models, we introduce M-estimators and M-tests, which are robust to departures from normality. In addition, we study the asymptotic properties and consider a computational algorithm for these estimators.

KW - Asymptotic normality

KW - Efficiency

KW - M-estimators

KW - M-tests

KW - Uniform asymptotic linearity

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M-procedures in the general multivariate nonlinear regression model

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