Regression calibration with more surrogates than mismeasured variables

Mathematics

• Logistic Regression 100%
• Superiority 66%
• Maximum Likelihood 66%
• Approximates 33%
• Linear Combination 33%
• Covariate 33%
• Logistic Regression Model 33%
• Parameter Space 33%
• Explanatory Variable 33%
• Conditional Expectation 33%
• Calibration Model 33%
• Estimated Slope 33%
• Asymptotic Variance 33%
• Data Structure 33%
• Good Approximation 33%
• Measurement Error 33%
• Observed Data 33%
• Nuisance Parameter 33%

Keyphrases

• Regression Calibration 100%
• Logistic Regression 37%
• Maximum Likelihood 25%
• Two-stage Approach 25%
• Multiple Surrogates 25%
• Sensitivity Analysis 12%
• Explanatory Variables 12%
• Finite Sample 12%
• Parameter Space 12%
• Linear Combination 12%
• Logistic Regression Model 12%
• Scalar 12%
• Milton 12%
• Measurement Error 12%
• Approximate Model 12%
• Troubling 12%
• Single Dataset 12%
• Spiegelman 12%
• Statistical Inference 12%
• Reparameterization 12%
• Natural Form 12%
• Conditional Expectation 12%
• Calibration Approach 12%
• Calibration Model 12%
• Asymptotic Variance 12%
• Asymptotically Equivalent 12%
• Statistical Planning 12%
• Nuisance Parameter 12%
• Sampling Simulation 12%