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

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

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.

Original language	English
Pages (from-to)	1-13
Number of pages	13
Journal	Pakistan Journal of Statistics
Volume	26
Issue number	1
State	Published - Jan 2010
Externally published	Yes

Keywords

Asymptotic normality
Efficiency
M-estimators
M-tests
Uniform asymptotic linearity

Cite this

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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 - Sanhueza, Antonio

AU - Sen, Pranab K.

AU - Araneda, Nelson

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N2 - 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.

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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JO - Pakistan Journal of Statistics

JF - Pakistan Journal of Statistics

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M-procedures in the general multivariate nonlinear regression model

Abstract

Keywords

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