Rate My Professor Xiaoyan Lin

Lin's research centers on Bayesian statistics with applications in survival analysis and medical diagnostic accuracy and ROC analysis. She develops advanced methods for censored and ordinal data, including latent graphical models for dependence among ordinal variables, neural networks for ordinal classification, and Bayesian Additive Regression Trees for arbitrary censored survival data. Her key publications encompass 'Bayesian proportional hazards model for current status data with monotone splines' (Computational Statistics & Data Analysis, 2011), 'A semiparametric probit model for case 2 interval-censored failure time data' (Statistics in Medicine, 2010), 'A Bayesian proportional hazards model for general interval-censored data' (Lifetime Data Analysis, 2015), 'Bayesian hierarchical latent class models for estimating diagnostic accuracy' (Statistical Methods in Medical Research, 2020), 'Bayesian semiparametric model for spatially correlated interval-censored survival data' (Computational Statistics & Data Analysis, 2014), 'Bayesian proportional odds models for analyzing current status data: univariate, clustered, and multivariate' (Communications in Statistics-Simulation and Computation, 2011), and 'A Bayesian approach for analyzing case 2 interval-censored data under the semiparametric proportional odds model' (Statistics & Probability Letters, 2011). Lin has received the Superior Graduate Student Achievement Award from the University of Missouri Graduate School (2006-2007), a travel award to the Joint Statistical Meeting (2007), and three first-class scholarships as an undergraduate. She serves her department as librarian representative since 2010, course coordinator for STAT 515 since 2011, qualifying exam committee member, journal reviewer for outlets including Computational Statistics and Data Analysis and Bayesian Analysis, and advisor or committee member for several Ph.D. students.