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Using Logistic Regression and Variable Selection to Model Time-To-Event Data:Applications to Tree Phenology and Graduation Time of Engineers

Burström, Jesse (2013) MASM01 20131
Mathematical Statistics
Abstract (Swedish)
The day of bud burst (DBB) and leaf senescence are two examples of
time-to-event phenological processes influenced by climate factors. Time
to graduation or quitting for engineering students is another example of
time-to-event data, with the added complication of having multiple
possible outcomes, or absorbing states. This master thesis elaborates upon
the models presented in Song (2010) "Stochastic Process Based Regression
Modeling of Time-to-event Data". The time-to-event model is extended to
use many different covariates, and Lasso regularization techniques are
used for variable selection, resulting in compact and statistically
relevant models. Models with multiple outcomes are shown to be able to
perform... (More)
The day of bud burst (DBB) and leaf senescence are two examples of
time-to-event phenological processes influenced by climate factors. Time
to graduation or quitting for engineering students is another example of
time-to-event data, with the added complication of having multiple
possible outcomes, or absorbing states. This master thesis elaborates upon
the models presented in Song (2010) "Stochastic Process Based Regression
Modeling of Time-to-event Data". The time-to-event model is extended to
use many different covariates, and Lasso regularization techniques are
used for variable selection, resulting in compact and statistically
relevant models. Models with multiple outcomes are shown to be able to
perform classification of students sequentially over time. For the
phenological examples, DBB is predicted with an accuracy of a couple of
days while leaf senescence proves to be a harder problem, possibly in need
of additional climate data not included in this analysis. Overall the
model of Song is shown to have great promise and versatility for modeling
of time-to-event data. (Less)
Please use this url to cite or link to this publication:
author
Burström, Jesse
supervisor
organization
course
MASM01 20131
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
3915077
date added to LUP
2013-07-01 14:42:45
date last changed
2013-07-01 14:42:45
@misc{3915077,
  abstract     = {The day of bud burst (DBB) and leaf senescence are two examples of 
time-to-event phenological processes influenced by climate factors. Time 
to graduation or quitting for engineering students is another example of 
time-to-event data, with the added complication of having multiple 
possible outcomes, or absorbing states. This master thesis elaborates upon 
the models presented in Song (2010) "Stochastic Process Based Regression 
Modeling of Time-to-event Data". The time-to-event model is extended to 
use many different covariates, and Lasso regularization techniques are 
used for variable selection, resulting in compact and statistically 
relevant models. Models with multiple outcomes are shown to be able to 
perform classification of students sequentially over time. For the 
phenological examples, DBB is predicted with an accuracy of a couple of 
days while leaf senescence proves to be a harder problem, possibly in need 
of additional climate data not included in this analysis. Overall the 
model of Song is shown to have great promise and versatility for modeling 
of time-to-event data.},
  author       = {Burström, Jesse},
  language     = {eng},
  note         = {Student Paper},
  title        = {Using Logistic Regression and Variable Selection to Model Time-To-Event Data:Applications to Tree Phenology and Graduation Time of Engineers},
  year         = {2013},
}