Efficient Estimation for Semiparametric Structural Equation Models With Censored Data
Mr. Kin Yau WONG, Alex
University of North Carolina at Chapel Hill, USA
Structural equation modeling is commonly used to capture complex structures of relationships among multiple variables, both latent and observed. In this talk, I describe a general class of structural equation models with a semiparametric component for potentially censored survival times. I consider nonparametric maximum likelihood estimation and propose a combined Expectation-Maximization and Newton-Raphson algorithm for its computation. I discuss conditions for model identifiability and present results on the consistency, asymptotic normality, and semiparametric efficiency of the estimators. Finally, I demonstrate the satisfactory performance of the proposed methods through simulation studies and provide applications to a motivating cancer study from The Cancer Genome Atlas that contains a variety of genomic variables.
Kin Yau Wong is a PhD student in the Department of Biostatistics at the University of North Carolina at Chapel Hill (UNC), studying under the supervision of Drs. Danyu Lin and Donglin Zeng. He is interested in developing novel statistical methods to solve problems arising from modern biomedical and public health research. In particular, his researchis focused on the area of integrative analysis of genomic data. He is also a graduate research assistant at the Perou Lab of the Department of Genetics at UNC. Kin Yau grew up in Hong Kong, where he obtained the Bachelor of Science (Actuarial Science) and Master of Philosophy degrees from the University of Hong Kong
Date: 10 March, 2017 (Friday)
Time: 11:00 am – 12:00 noon
Venue: Room 7-207, 7/F, Lau Ming Wai Academic Building, City University of Hong Kong