Null intercept measurement error regression models

DOI10.1007/BF02595707zbMath1016.62081OpenAlexW2023578269MaRDI QIDQ1872849

Heleno Bolfarine, Reiko Aoki, Julio da Motta Singer

Publication date: 18 May 2003

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02595707

zbMATH Keywords

random effectsFisher information matrixcorrelated model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Asymptotic properties of parametric tests (62F05)

Related Items (16)

A modified signed likelihood ratio test in elliptical structural models ⋮ Bayesian analysis for a skew extension of the multivariate null intercept measurement error model ⋮ Inference and local influence assessment in skew-normal null intercept measurement error model ⋮ Hypotheses testing for structural calibration model ⋮ Bayesian analysis of skew-\(t\) multivariate null intercept measurement error model ⋮ Skew-normal distribution in the multivariate null intercept measurement error model ⋮ A robust Bayesian approach to null intercept measurement error model with application to dental data ⋮ Statistical analysis of proficiency testing results under elliptical distributions ⋮ Adjusted Likelihood Inference in an Elliptical Multivariate Errors-in-Variables Model ⋮ Local influence in null intercept measurement error regression under a student_t model ⋮ Regression models for pretest/posttest data in blocks ⋮ Bayesian Analysis of a Multivariate Null Intercept Errors-in-Variables Regression Model ⋮ Modified likelihood ratio tests in heteroskedastic multivariate regression models with measurement error ⋮ Hypothesis testing in the unrestricted and restricted parametric spaces of structural models ⋮ Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model ⋮ Bayes Factors for Comparison of Restricted Simple Linear Regression Coefficients

Cites Work

• On the maximum likelihood estimation of a linear structural relationship when the intercept is known
• Regression Models for the Analysis of Pretest/Posttest Data
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