Using Logistic Regression and Variable Selection to Model Time-To-Event Data:Applications to Tree Phenology and Graduation Time of Engineers
(2013) MASM01 20131Mathematical 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:
http://lup.lub.lu.se/student-papers/record/3915077
- author
- Burström, Jesse
- supervisor
- organization
- course
- MASM01 20131
- year
- 2013
- 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}}, }