Assessing Influence in Multiple Linear Regression with Incomplete Data
From MaRDI portal
Publication:3028114
DOI10.2307/1269078zbMath0625.62051OpenAlexW2013944982MaRDI QIDQ3028114
No author found.
Publication date: 1986
Full work available at URL: https://doi.org/10.2307/1269078
EM algorithmmaximum likelihood estimationincomplete datadiagnosticsmultiple linear regressionCooks's distance measureone-step influence measure
Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Probabilistic methods, stochastic differential equations (65C99)
Related Items (13)
The internal norm approach to influence diagnostics ⋮ ML estimation of the multivariate \(t\) distribution and the EM algorithm ⋮ Influence of incomplete observations in multiple linear regression ⋮ Randomized extrapolation for accelerating EM-type fixed-point algorithms ⋮ On the ridge regression estimator with sub-space restriction ⋮ Case-deletion Influence Measures for the Data from Multivariate t Distributions ⋮ Influence analysis of additive mixed-effects nonlinear regression models via EM algorithm ⋮ A solution to multiple linear regression problems with ordered attributes ⋮ Approximating the internal norm influence measure in linear regression ⋮ Quadratic extrapolation for accelerating convergence of the EM fixed point problem ⋮ Valid properties of truncated Student-\(t\) regression model with applications in analysis of censored data ⋮ On robust linear regression with incomplete data ⋮ A new formulation of stress-strength reliability in a regression setup
This page was built for publication: Assessing Influence in Multiple Linear Regression with Incomplete Data