Influence measures in ridge regression when the error terms follow an AR(1) process

DOI10.1007/s00180-015-0615-5zbMath1347.65032OpenAlexW1959989938MaRDI QIDQ311286

Tuğba Söküt Açar, M. Revan Özkale

Publication date: 29 September 2016

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-015-0615-5

zbMATH Keywords

autocorrelationmulticollinearityregression diagnosticsridge estimatorgeneralized least squares estimator

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items

Regression diagnostics methods for Liu estimator under the general linear regression model, Influence measures based on confidence ellipsoids in general linear regression model with correlated regressors, Cross validation of ridge regression estimator in autocorrelated linear regression models

Uses Software

• alr3

Cites Work

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• Regression diagnostics in an autocorrelated model
• Assessing global influential observations in modified ridge regression
• Local influence in ridge regression
• On the performance of biased estimators in the linear regression model with correlated or heteroscedastic errors
• Leverages and Influential Observations in a Regression Model with Autocorrelated Errors
• A Simulation Study of Ridge Regression Estimators with Autocorrelated Errors
• Regressor space outliers in ridge regression
• A Monte Carlo Evaluation of Some Ridge-Type Estimators
• Detection of Influential Observation in Linear Regression
• A Simple Derivation of Deletion Diagnostic Results for the General Linear Model with Correlated Errors
• Leverage and cochrane-orcutt estimation in linear regression
• Influence measures in affine combination type regression
• Ridge Regression: Biased Estimation for Nonorthogonal Problems