scholarly journals A shared-parameter continuous-time hidden Markov and survival model for longitudinal data with informative dropout

2018 ◽  
Vol 38 (6) ◽  
pp. 1056-1073 ◽  
Author(s):  
Francesco Bartolucci ◽  
Alessio Farcomeni
Keyword(s):  
Longitudinal Data ◽  
Continuous Time ◽  
Hidden Markov ◽  
Survival Model ◽  
Informative Dropout

Continuous time hidden Markov model for longitudinal data

2020 ◽  
Vol 179 ◽  
pp. 104646
Author(s):  
Jie Zhou ◽  
Xinyuan Song ◽  
Liuquan Sun
Keyword(s):  
Longitudinal Data ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Continuous Time ◽  
Hidden Markov

Finite-dimensional models for hidden Markov chains

1995 ◽  
Vol 27 (01) ◽  
pp. 146-160
Author(s):  
Lakhdar Aggoun ◽  
Robert J. Elliott
Keyword(s):  
Markov Chains ◽  
Expectation Maximization ◽  
Continuous Time ◽  
Hidden Markov ◽  
Expectation Maximization Algorithm ◽  
Linear Filtering ◽  
Occupation Times ◽  
Finite Dimensional ◽  
Self Tuning ◽  
Filtering Problem

A continuous-time, non-linear filtering problem is considered in which both signal and observation processes are Markov chains. New finite-dimensional filters and smoothers are obtained for the state of the signal, for the number of jumps from one state to another, for the occupation time in any state of the signal, and for joint occupation times of the two processes. These estimates are then used in the expectation maximization algorithm to improve the parameters in the model. Consequently, our filters and model are adaptive, or self-tuning.

An S-Plus Implementation of Hidden Markov Models in Continuous Time

2000 ◽  
Vol 9 (4) ◽  
pp. 621 ◽  
Author(s):  
Alexandre Bureau ◽  
James P. Hughes ◽  
Stephen C. Shiboski
Keyword(s):  
Hidden Markov Models ◽  
Continuous Time ◽  
Markov Models ◽  
Hidden Markov

scholarly journals Use of OSWALD for analyzing longitudinal data with informative dropout

2007 ◽  
Vol 85 (2) ◽  
pp. 109-114 ◽  
Author(s):  
Amy E. Begley ◽  
Gong Tang ◽  
Sati Mazumdar ◽  
Patricia R. Houck ◽  
John Scott ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Informative Dropout
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