Rank Scores for Linear Models under Asymmetric Distributions

Regression rank scores in nonlinear models

with xi ∈ Rk, θ = (θ0, θ1, . . . , θp)′ ∈ Θ (compact in Rp+1), where g(x, θ) = θ0 + g̃(x, θ1, . . . , θp) is continuous, twice differentiable in θ and monotone in components of θ. Following Gutenbrunner and Jurečková (1992) and Jurečková and Procházka (1994), we introduce regression rank scores for model (1), and prove their asymptotic properties under some regularity conditions. As an applicati...

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 ...

Rank tests for corrupted linear models

For some variants of regression models, including partial, measurement error or error-in-variables, latent effects, semi-parametric and otherwise corrupted linear models, the classical parametric tests generally do not perform well. Various modifications and generalizations considered extensively in the literature rests on stringent regularity assumptions which are not likely to be tenable in m...

Linear Models for Functional Data

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Reduced-rank Vector Generalized Linear Models