Seemingly Unrelated Measurement Error Models, with Application to Nutritional Epidemiology

DOI10.1111/j.1541-0420.2005.00400.xzbMath1091.62110OpenAlexW2015258386WikidataQ47600067 ScholiaQ47600067MaRDI QIDQ5473209

Victor Kipnis, Laurence S. Freedman, Douglas Midthune, Raymond J. Carroll

Publication date: 20 June 2006

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2005.00400.x

zbMATH Keywords

model selectionAkaike information criterionlatent variablesmeasurement errormixed modelsmodel averagingnutritional epidemiologyseemingly unrelated regressionBayes information criterion

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (9)

Seemingly unrelated nonparametric models with positive correlation and constrained error variances ⋮ Efficient estimation of varying coefficient seemly unrelated regression model ⋮ Two-stage estimation for seemingly unrelated nonparametric regression models ⋮ Efficient estimation of seemingly unrelated additive nonparametric regression models ⋮ A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model ⋮ Statistical inference on seemingly unrelated non-parametric regression models with serially correlated errors ⋮ Mixture of measurement errors and their impact on parameter inferences ⋮ A Seemingly Unrelated Nonparametric Additive Model with Autoregressive Errors ⋮ ASYMPTOTIC THEORY IN FIXED EFFECTS PANEL DATA SEEMINGLY UNRELATED PARTIALLY LINEAR REGRESSION MODELS

Cites Work

• Variances Are Not Always Nuisance Parameters
• Bias Analysis and SIMEX Approach in Generalized Linear Mixed Measurement Error Models
• Frequentist Model Average Estimators
• Errors of Measurement in Statistics
• MODEL SELECTION AND INFERENCE: FACTS AND FICTION
• An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias

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