Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational un...
This paper discusses Bayesian inference in change-point models. Current approaches place a possibly ...
In this dissertation we consider some novel applications of Bayesian longitudinal methods. As infere...
This thesis presents the new methodological approach for carrying out Bayesian inference of the Dyna...
Most statistical analysis, theory and practice, is concerned with static models; models with a propo...
Many processes evolve over time and statistical models need to be adaptive to change. This thesis pr...
Analyses involving both longitudinal and time-to-event data are quite common in medical research. Th...
Time series-data accompanied with a sequential ordering-occur and evolve all around us. Analysing ti...
We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data m...
"Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitu...
This thesis considers the incorporation and deletion of information in Dynamic Linear Models togethe...
The statistical analysis of the information generated by medical follow-up is a very important chall...
The context of comparing two different groups of subjects that are measured repeatedly over time is ...
This thesis focuses on the application of the hierarchical Bayesian (HB) methodology to real data. T...
The paper investigates a Bayesian hierarchical model for the analysis of longitudinal data from a r...
<div><p>The joint modeling of longitudinal and time-to-event data is an active area of statistics re...
This paper discusses Bayesian inference in change-point models. Current approaches place a possibly ...
In this dissertation we consider some novel applications of Bayesian longitudinal methods. As infere...
This thesis presents the new methodological approach for carrying out Bayesian inference of the Dyna...
Most statistical analysis, theory and practice, is concerned with static models; models with a propo...
Many processes evolve over time and statistical models need to be adaptive to change. This thesis pr...
Analyses involving both longitudinal and time-to-event data are quite common in medical research. Th...
Time series-data accompanied with a sequential ordering-occur and evolve all around us. Analysing ti...
We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data m...
"Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitu...
This thesis considers the incorporation and deletion of information in Dynamic Linear Models togethe...
The statistical analysis of the information generated by medical follow-up is a very important chall...
The context of comparing two different groups of subjects that are measured repeatedly over time is ...
This thesis focuses on the application of the hierarchical Bayesian (HB) methodology to real data. T...
The paper investigates a Bayesian hierarchical model for the analysis of longitudinal data from a r...
<div><p>The joint modeling of longitudinal and time-to-event data is an active area of statistics re...
This paper discusses Bayesian inference in change-point models. Current approaches place a possibly ...
In this dissertation we consider some novel applications of Bayesian longitudinal methods. As infere...
This thesis presents the new methodological approach for carrying out Bayesian inference of the Dyna...