Multivariate survival data are characterized by the presence of correlation between event times within the same cluster. First, we build multi-dimensional copulas with flexible and possibly symmetric dependence structures for such data. In particular, clustered right-censored survival data are modeled using mixtures of max-infinitely divisible bivariate copulas. Second, these copulas are fit by a likelihood approach where the vast amount of copula derivatives present in the likelihood is approximated by finite differences. Third, we formulate conditions for clustered right-censored survival data under which an information criterion for model selection is either weakly consistent or consistent. Several of the familiar selection criteria are ...
The introduction of copulas, which allow separating the dependence structure of a multivariate distr...
This book introduces readers to advanced statistical methods for analyzing survival data involving c...
Generating survival data with a clustered and multi-state structure is useful to study finite sample...
Multivariate survival data are characterized by the presence of correlation between event times with...
For the analysis of clustered survival data, two different types of model that take the association ...
Bivariate survival outcomes arise frequently in applied studies where the occurrence of two events o...
This article proposes an approach to estimate and make inference on the parameters of copula link-ba...
Bivariate survival outcomes arise frequently in applied studies where the occurrence of two events o...
The main aim of this work is the study of clustering dependent data by means of copula functions. Co...
Motivated by the breast cancer survivorship research program at BC Cancer Agency, this dissertation ...
In a survival study, it may not be possible to record the exact event time but only that the event h...
The majority of model-based clustering techniques is based on multivariate normal models and their v...
Thesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Biostatistics and ...
Generating survival data with a clustered and multi-state structure is useful to study multi-state m...
In this dissertation we solve the nonidentifiability problem of Archimedean copula models based on d...
The introduction of copulas, which allow separating the dependence structure of a multivariate distr...
This book introduces readers to advanced statistical methods for analyzing survival data involving c...
Generating survival data with a clustered and multi-state structure is useful to study finite sample...
Multivariate survival data are characterized by the presence of correlation between event times with...
For the analysis of clustered survival data, two different types of model that take the association ...
Bivariate survival outcomes arise frequently in applied studies where the occurrence of two events o...
This article proposes an approach to estimate and make inference on the parameters of copula link-ba...
Bivariate survival outcomes arise frequently in applied studies where the occurrence of two events o...
The main aim of this work is the study of clustering dependent data by means of copula functions. Co...
Motivated by the breast cancer survivorship research program at BC Cancer Agency, this dissertation ...
In a survival study, it may not be possible to record the exact event time but only that the event h...
The majority of model-based clustering techniques is based on multivariate normal models and their v...
Thesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Biostatistics and ...
Generating survival data with a clustered and multi-state structure is useful to study multi-state m...
In this dissertation we solve the nonidentifiability problem of Archimedean copula models based on d...
The introduction of copulas, which allow separating the dependence structure of a multivariate distr...
This book introduces readers to advanced statistical methods for analyzing survival data involving c...
Generating survival data with a clustered and multi-state structure is useful to study finite sample...