Efficient Estimation of the Varying-Coefficient Partially Linear Proportional Odds Models with Current Status Data
Abstract
We consider a varying-coefficient partially linear proportional odds model with current status data. This model enables one to examine the extent to which some covariates interact nonlinearly with an exposure variable, while other covariates present linear effects. B-spline approach and sieve maximum likelihood estimation method are used to get an integrated estimate for the linear coefficients, the varying-coefficient functions and the baseline function. The proposed parameter estimators are proved to be consistent and asymptotically normal, and the estimators for the nonparametric functions achieve the optimal rate of convergence. Simulation studies and a real data analysis are used for assessment and illustration.
Description
Keywords
Statistics
Citation
Lu, S. (2016). Efficient Estimation of the Varying-Coefficient Partially Linear Proportional Odds Models with Current Status Data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25843