Sensitivity analysis of $M$-estimators of non-linear regression models

Asunción Rubio; Francisco Quintana; Jan Ámos Víšek

Displaying similar documents to “Sensitivity analysis of $M$ -estimators of non-linear regression models”

$M$ -estimation in nonlinear regression for longitudinal data

Martina Orsáková (2007)

Kybernetika

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The longitudinal regression model $Z_{i}^{j} = m (θ_{0}, 𝕏_{i} (T_{i}^{j})) + ε_{i}^{j},$ where $Z_{i}^{j}$ is the $j$ th measurement of the $i$ th subject at random time $T_{i}^{j}$ , $m$ is the regression function, $𝕏_{i} (T_{i}^{j})$ is a predictable covariate process observed at time $T_{i}^{j}$ and $ε_{i}^{j}$ is a noise, is studied in marked point process framework. In this paper we introduce the assumptions which guarantee the consistency and asymptotic normality of smooth $M$ -estimator of unknown parameter $θ_{0}$ .

The least trimmed squares. Part III: Asymptotic normality

Jan Ámos Víšek (2006)

Kybernetika

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Asymptotic normality of the least trimmed squares estimator is proved under general conditions. At the end of paper a discussion of applicability of the estimator (including the discussion of algorithm for its evaluation) is offered.

Asymptotics of the regression quantile basic solution under misspecification

Keith Knight (2008)

Applications of Mathematics

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We consider the asymptotic distribution of covariate values in the quantile regression basic solution under weak assumptions. A diagnostic procedure for assessing homogeneity of the conditional densities is also proposed.

Displaying similar documents to “Sensitivity analysis of M -estimators of non-linear regression models”

M -estimation in nonlinear regression for longitudinal data

The least trimmed squares. Part III: Asymptotic normality

Asymptotics of the regression quantile basic solution under misspecification

Displaying similar documents to “Sensitivity analysis of $M$ -estimators of non-linear regression models”

$M$ -estimation in nonlinear regression for longitudinal data