Estimation of a Linear Function for a Calibration Line; Consideration of a Recent Proposal

By J. Berkson. 1969

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1. Ching-Chung Kuo
2. Vining G. Geoffrey
3. J. Szarka
4. Nels G. Johnson
5. G. Vining
6. P. Parker
7. S. R. Wilson

Citations in the corpus

• Error asymmetry in causal and anticausal regression (2016, neutral): “However, this conclusion was criticized by many researchers (Berkson 1969; Halperin 1970; Parker et al. 2010) who argued that the conclusions made based on reverse regression do not reflect the actual relationship between cause and effect, which is obvious regarding (4). So far, a statement about th”
• A comparison of two statistical estimators in inverse prediction (2012, background): “Source Methodology xC xI Evaluation criterion Krutchkoff (1967) Analytical/simulation √ MSE (when n is small or x is close to x ) Berkson (1969) Analytical √ MSE (when n is large or x is far away from x ), CST, MLE Halperin (1970) Analytical √ PC (when x is far away from x ) Martinelle (1970) Analyt”
• The Prediction Properties of Classical and Inverse Regression for the Simple Linear Calibration Problem (2010, methodology): “Berkson (1969) and Halperin (1970) present significant criticisms to Krutchkoff’s work. Centner et al. (1998) compared classical calibration and inverse regression and recommended inverse regression based on the meansquared error of predicting x̂. They also found that the improvement due to the use ”
• The Prediction Properties of Inverse and Reverse Regression for the Simple Linear Calibration Problem (2010, methodology): “Berkson (1969) and Halperin (1970) showed that for large samples, the reverse estimator has smaller mean squared error only when x lies within a small interval around x. Graybill (1976) and Seber (1977) developed a (1 − α)100% confidence region for an unknown value of x using the inverse regression ”