Bayes Prediction for a Heteroscedastic Regression Superpopulation Model Using Balanced Loss Function

DOI10.1080/03610920601125797zbMath1315.62026OpenAlexW2059301308MaRDI QIDQ3593575

Priyanka Aggarwal, Ashok K. Bansal

Publication date: 23 July 2007

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920601125797

zbMATH Keywords

Lindley's approximationparabolic cylinder functionbalanced loss function (BLF)Bayes predictive expected lossgeneralized inverse normal (GIN)

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15) Sampling theory, sample surveys (62D05)

Related Items (8)

Bayesian and robust Bayesian analysis under a general class of balanced loss functions ⋮ Unnamed Item ⋮ Optimal and minimax prediction in multivariate normal populations under a balanced loss function ⋮ All admissible linear predictors in the finite populations with respect to inequality constraints under a balanced loss function ⋮ Bayesian inference of three-parameter bathtub-shaped lifetime distribution ⋮ Robustness of Bayes Prediction Under Error-in-Variables Superpopulation Model ⋮ Bayes Prediction for a Stratified Regression Superpopulation Model Using Balanced Loss Function ⋮ Linear minimax prediction of finite population regression coefficient under a balanced loss function

Cites Work

• Generalized inverse normal distributions
• Estimation of functions of population means and regression coefficients including structural coefficients. A minimum expected loss (MELO) approach
• Regression Analysis when the Variance of the Dependent Variable is Proportional to the Square of its Expectation
• Bayes Predictor of One-Parameter Exponential Family Type Population Mean Under Balanced Loss Function
• A note on finite population prediction under asymetric loss functions
• Minimax prediction in finite populations
• Estimating the Mean of a Normal Distribution with Known Coefficient of Variation

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