Rank-Based Analyses of Linear Models UsingR

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 ...

High Breakdown Rank-Based Estimates for Linear Models

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...

Reduced-rank Vector Generalized Linear Models