Lu, XuewenZhang, Ce2020-08-262020-08-262020-08-14Zhang, C. (2020). Efficient Estimation of the Additive Hazards Model with Bivariate Current Status Data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/112429In this thesis, we present sieve maximum likelihood estimators of the both finite and infinite dimensional parameters in the marginal additive hazards model with bivariate current status data, where the joint distribution of the bivariate survival times is modeled by a copula. We assume the two baseline hazard functions and the copula are unknown functions, and use constrained Bernstein polynomials to approximate these functions. Compared with the existing methods for estimation of the copula models for bivariate survival data, the proposed new method has two main advantages. First, our method does not need to specify the form of the copula model and is more flexible. Second, the proposed estimators have strong consistency, optimal rate of convergence and the regression parameter estimator is asymptotically normal and semi-parametrically efficient. Simulation studies reveal that the proposed estimators have good finite-sample properties. Finally, a real data application is provided for illustration.University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Education--SciencesPhysics--TheoryStatisticsmaster thesis10.11575/PRISM/38111