Date of Award
Doctor of Philosophy (PhD)
Staudenmayer, John W.
Buonaccorsi, John P.
Conlon, Erin M.
Confidence interval, Corrected score functions, Misclassification, Mixture model, Proportion, Regression calibration
Mathematics | Statistics and Probability
When categorical data are misplaced into the wrong category, we say the data is affected by misclassification. This is common for data collection. It is well-known that naive estimators of category probabilities and coefficients for regression that ignore misclassification can be biased. In this dissertation, we develop methods to provide improved estimators and confidence intervals for a proportion when only a misclassified proxy is observed, and provide improved estimators and confidence intervals for regression coefficients when only misclassified covariates are observed. Following the introduction and literature review, we develop two estimators for a proportion , one which reduces the bias, and one with smaller mean square error. Then we will give two methods to find a confidence interval for a proportion, one using optimization techniques, and the other one using Fieller's method. After that, we will focus on developing methods to find corrected estimators for coefficients of regression with misclassified covariates, with or without perfectly measured covariates, and with a known estimated misclassification/reclassification model. These correction methods use the score function approach, regression calibration and a mixture model. We also use Fieller's method to find a confidence interval for the slope of simple regression with misclassified binary covariates. Finally, we use simulation to demonstrate the performance of our proposed methods.
Shieh, Meng-Shiou, "Correction Methods, Approximate Biases, and Inference for Misclassified Data" (2009). Open Access Dissertations. Paper 61.