Topics in Statistics with Applications

Columbia University, New York NY

Investigators

Abstract

Proposal ID: 0203798 PI: Zhiliang Ying Title: Topics in statistics with applications Abstract This project addresses issues related to semiparametric regression models with censored data as well as testing and estimation of item differential functioning arising from IRT-based standardized tests. The investigator and his collaborators develop new algorithms for obtaining parameter estimators for the accelerated failure time regression. These algorithms are based on linear programming which is easy to implement. The estimators are shown to be asymptotically normal, with the limiting variance estimated via a simple resampling method. The approach is extended to censored quantile regression models, which are common in econometrics literature. They study the class of semiparametric transformation models via estimating equations, resulting in a general method for parameter estimation and inference, justified by a large sample theory. They develop a new approach for adaptive standardized tests to detect possible item differential functioning via a generalized logistic regression model with measurement errors. The statistical problems dealt with here are motivated by applications in biomedical sciences, sociology, economics, marketing, psychology and education. The project develops appropriate statistical models, computer algorithms and mathematical theory. The results can be used to facilitate design of clinical trials and epidemiological studies, particularly in studies of cancer, cardiovascular diseases and AIDS, to analyze unemployment duration data, to quantify consumer responses to marketing strategies and to screen out items for use in large-scale standardized tests that are tilted against certain group or groups to ensure fairness