Residuals for the Linear Model With General Covariance Structure
From MaRDI portal
Publication:4214231
DOI10.1111/1467-9868.00119zbMath0909.62071OpenAlexW1987014471MaRDI QIDQ4214231
Publication date: 8 April 1999
Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/1467-9868.00119
krigingtime seriescross-validationgeostatisticsgeneralized least squaresresidualsMahalanobis distance
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Generalized linear models (logistic models) (62J12) Diagnostics, and linear inference and regression (62J20)
Related Items (17)
Influence on tests with focus on linear models ⋮ multiple and conditional deletion diagnostics for general linear models ⋮ Influence diagnostics and outlier tests for varying coefficient mixed models ⋮ A unified approach on residuals, leverages and outliers in the linear mixed model ⋮ Outlier detection and accommodation in general spatial models ⋮ Outlier detection and accommodation in meta-regression models ⋮ Multiple Cases Deletion Diagnostics for Linear Mixed Models ⋮ Evaluation of the mixed linear model with orthogonalized and Studentized residuals ⋮ Deletion, replacement and mean-shift for diagnostics in linear mixed models ⋮ Detection of outliers in multilevel models ⋮ Estimation of regression parameters in the presence of outliers in the response ⋮ Influence Diagnostics on Testing Linear Hypothesis in Linear Models with Correlated Errors ⋮ Application of ‘Delete = Replace’ to Deletion Diagnostics for Variance Component Estimation in the Linear Mixed Model ⋮ Robust methods for the analysis of spatially autocorrelated data ⋮ Bounds for a multivariate extension of range over standard deviation based on the Mahalanobis distance ⋮ Robust Estimation in Generalized Linear Mixed Models ⋮ Residuals and leverages in the linear mixed measurement error models
This page was built for publication: Residuals for the Linear Model With General Covariance Structure
