Bayesian Transformation Models for Multivariate Survival Data
نویسندگان
چکیده
منابع مشابه
Bayesian Graphical Models for Multivariate Functional Data
Graphical models express conditional independence relationships among variables. Although methods for vector-valued data are well established, functional data graphical models remain underdeveloped. By functional data, we refer to data that are realizations of random functions varying over a continuum (e.g., images, signals). We introduce a notion of conditional independence between random func...
متن کاملBayesian multivariate hierarchical transformation models for ROC analysis.
A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, a...
متن کاملGamma frailty transformation models for multivariate survival times.
We propose a class of transformation models for multivariate failure times. The class of transformation models generalize the usual gamma frailty model and yields a marginally linear transformation model for each failure time. Nonparametric maximum likelihood estimation is used for inference. The maximum likelihood estimators for the regression coefficients are shown to be consistent and asympt...
متن کاملSemiparametric transformation models for semicompeting survival data.
Semicompeting risk outcome data (e.g., time to disease progression and time to death) are commonly collected in clinical trials. However, analysis of these data is often hampered by a scarcity of available statistical tools. As such, we propose a novel semiparametric transformation model that improves the existing models in the following two ways. First, it estimates regression coefficients and...
متن کاملBayesian Semiparametric Methods for Longitudinal, Multivariate, and Survival Data
MICHAEL LINDSEY PENNELL: BAYESIAN SEMIPARAMETRIC METHODS FOR LONGITUDINAL, MULTIVARIATE, AND SURVIVAL DATA. (Under the direction of Dr. David Dunson.) In many biomedical studies, the observed data may violate the assumptions of standard parametric methods. In these situations, Bayesian methods are appealing since nonparametric priors, such as the Dirichlet process (DP), can incorporate a priori...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Scandinavian Journal of Statistics
سال: 2013
ISSN: 0303-6898
DOI: 10.1111/sjos.12010