Rfit: Rank-based Estimation for Linear Models

Rfit: Rank-based Estimation for Linear Models

In the nineteen seventies, Jurečková and Jaeckel proposed rank estimation for linear models. Since that time, several authors have developed inference and diagnostic methods for these estimators. These rank-based estimators and their associated inference are highly efficient and are robust to outliers in response space. The methods include estimation of standard errors, tests of general linear ...

histopathological effects of cypermethrin on liver of aphanius sophiae (heckel, 1849) using rank-based estimation for linear models

ecotoxicological studies that use histological techniques enjoy descriptive approach in explaining the damages to tissues. in the present study, a rank-based estimation for linear models was used to examine significant difference between levels of cypermethrin on histological changes of the liver in aphanius sophiae . the toxicity of cypermethrin associated with certain environmental factors su...

Rank Estimation of Partially Linear Index Models

We consider a generalized regression model with a partially linear index. The index contains an additive nonparametric component in addition to the standard linear component, and the model’s dependent variable is transformed by a unknown monotone function. We propose weighted rank estimation procedures for estimating (i) the coe¢ cients for the linear component, (ii) the nonparametric component...

A Rank-based Estimation Procedure For Linear Models With Clustered Data

Rank-Based Estimation and Associated Inferences for Linear Models with Cluster Correlated Errors

R estimators based on the joint ranks (JR) of all the residuals have been developed over the last twenty years for fitting linear models with independently distributed errors. In this paper, we extend these estimators to estimating the fixed effects in a linear model with cluster correlated continuous error distributions for general score functions. We discuss the asymptotic theory of the estim...