Oblique Survival Trees in Discrete Event Time Analysis

2020 ◽  
Vol 24 (1) ◽  
pp. 247-258
Author(s):  
Malgorzata Kretowska
Keyword(s):  
Discrete Event ◽  
Time Analysis ◽  
Survival Trees ◽  
Event Time

Corrigendum to “Event time analysis of longitudinal neuroimage data” [NeuroImage 97 (15 August 2014) 9–18]

NeuroImage ◽  
2015 ◽  
Vol 108 ◽  
pp. 74
Author(s):  
Mert R. Sabuncu ◽  
Jorge L. Bernal-Rusiel ◽  
Martin Reuter ◽  
Douglas N. Greve ◽  
Bruce Fischl
Keyword(s):  
Time Analysis ◽  
Event Time

Survival curve estimation for informatively coarsened discrete event-time data

2007 ◽  
Vol 26 (10) ◽  
pp. 2184-2202 ◽  
Author(s):  
Michelle Shardell ◽  
Daniel O. Scharfstein ◽  
Samuel A. Bozzette
Keyword(s):  
Survival Curve ◽  
Discrete Event ◽  
Time Data ◽  
Curve Estimation ◽  
Event Time

scholarly journals Event-Time Analysis of Reproductive Traits of Dairy Heifers

1998 ◽  
Vol 81 (11) ◽  
pp. 2881-2889 ◽  
Author(s):  
B. Vargas ◽  
T. Van der Lende ◽  
M. Baaijen ◽  
J.A.M. Van Arendonk
Keyword(s):  
Reproductive Traits ◽  
Time Analysis ◽  
Event Time ◽  
Dairy Heifers

Hybrid System Model Simulation Framework for Cyber-Physical Systems

2011 ◽  
Vol 110-116 ◽  
pp. 4043-4049
Author(s):  
Soo Young Moon ◽  
Hyuk Park ◽  
Tae Ho Cho ◽  
Won Tae Kim
Keyword(s):  
Hybrid System ◽  
Fuzzy Inference ◽  
Model Simulation ◽  
Discrete Event ◽  
Cyber Physical Systems ◽  
System Specification ◽  
Simulation Framework ◽  
Physical Systems ◽  
Event Time ◽  
Ode Models

Most of existing frameworks for modeling and simulation of hybrid systems represent continuous behavior of systems using ordinary differential equations (ODEs). ODE models can be represented as discrete event system specification (DEVS) models through discretization and simulated in the DEVS simulation framework. However, in cyber-physical systems (CPS), it is difficult to represent the continuous behavior of a system using an ODE because it can have unknown, unpredictable variables. In that case, it is needed to predict the model’s next event time by inference to embed the model in a DEVS model. We propose the simulation framework in which a fuzzy inference module is added to each simulation model to determine its next event time. The proposed method enables simulation of hybrid system models which can or cannot be represented using an ODE.

scholarly journals Inference for cumulative incidence functions with informatively coarsened discrete event-time data

2008 ◽  
Vol 27 (28) ◽  
pp. 5861-5879 ◽  
Author(s):  
Michelle Shardell ◽  
Daniel O. Scharfstein ◽  
David Vlahov ◽  
Noya Galai
Keyword(s):  
Cumulative Incidence ◽  
Discrete Event ◽  
Time Data ◽  
Event Time ◽  
Cumulative Incidence Functions ◽  
Incidence Functions

Evaluation of a contract breeding management program in selected Ohio dairy herds with event-time analysis

2006 ◽  
Vol 77 (3-4) ◽  
pp. 145-160 ◽  
Author(s):  
Cheyney Meadows ◽  
Päivi J. Rajala-Schultz ◽  
Grant S. Frazer ◽  
Richard W. Meiring ◽  
Kent H. Hoblet
Keyword(s):  
Management Program ◽  
Dairy Herds ◽  
Time Analysis ◽  
Event Time ◽  
Breeding Management

scholarly journals Event time analysis of longitudinal neuroimage data

NeuroImage ◽  
2014 ◽  
Vol 97 ◽  
pp. 9-18 ◽  
Author(s):  
Mert R. Sabuncu ◽  
Jorge L. Bernal-Rusiel ◽  
Martin Reuter ◽  
Douglas N. Greve ◽  
Bruce Fischl
Keyword(s):  
Time Analysis ◽  
Event Time
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