An exact formula for the mean squared error of the inverse estimator in the linear calibration problem

DOI10.1016/0378-3758(85)90005-9zbMath0574.62058OpenAlexW2070028369MaRDI QIDQ1063355

Samuel D. Oman

Publication date: 1985

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0378-3758(85)90005-9

zbMATH Keywords

mean squared errorconfluent hypergeometric functionexact formulaexpectations of functions of a Poisson random variableinverse estimatorlinear calibration problem

Mathematics Subject Classification ID

Linear regression; mixed models (62J05)

Related Items (5)

Multivariate calibration: A generalization of the classical estimator ⋮ An effective approach to linear calibration estimation with its applications ⋮ Recurrence relations for noncentral density, distribution functions and inverse moments ⋮ Optimal designs for estimating the control values in multi-univariate regression models ⋮ THE CALIBRATION PROBLEM REVISITED

Cites Work

• A class of modified Stein estimators with easily computable risk functions
• Minimax estimators of the mean of a multivariate normal distribution
• A Bayesian Analysis of the Linear Calibration Problem
• An Analysis of the Linear-Calibration Controversy from the Perspective of Compound Estimation
• An explicit formula for the risk of James-Stein estimators
• Stein's Estimation Rule and Its Competitors--An Empirical Bayes Approach
• Classical and Inverse Regression Methods of Calibration in Extrapolation
• A Bayesian Look at Inverse Linear Regression
• On Inverse Estimation in Linear Regression
• On the Problem of Calibration
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