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Bud burst of birch in Finland and the United Kingdom - Logistic regression analysis and modeling

Burström, Jesse (2013) MASY01 20131
Mathematical Statistics
Abstract (Swedish)
The day of bud burst (DBB) of different tree species are known to be
affected by factors such as growing degree days and temperature. In this
paper a two state Markov chain is used to model DBB for birch. The model
is fit using logistic regression and LASSO regularization is used to
evaluate which of many potential factors best forecast DBB. Data of birch
from both Finland and the United Kingdom is studied and differences
between the models adapted to the two countries are investigated. For
modeling purposes to capture the environment of forecasting, estimated
interpolated gridded climate data was used and not directly measured
climate data.
It is found that the models give very accurate predictions on the DBB. For
Finland it is... (More)
The day of bud burst (DBB) of different tree species are known to be
affected by factors such as growing degree days and temperature. In this
paper a two state Markov chain is used to model DBB for birch. The model
is fit using logistic regression and LASSO regularization is used to
evaluate which of many potential factors best forecast DBB. Data of birch
from both Finland and the United Kingdom is studied and differences
between the models adapted to the two countries are investigated. For
modeling purposes to capture the environment of forecasting, estimated
interpolated gridded climate data was used and not directly measured
climate data.
It is found that the models give very accurate predictions on the DBB. For
Finland it is little more than 2 days in mean absolute error (MAE). The
model is also fairly compact having less than 10 explaining covariates.
The covariate, accumulated growing degree days, was as expected part of
the models as well as among others variation of precipitation. (Less)
Please use this url to cite or link to this publication:
author
Burström, Jesse
supervisor
organization
course
MASY01 20131
year
type
M2 - Bachelor Degree
subject
language
English
id
3430848
date added to LUP
2013-01-30 14:48:50
date last changed
2013-01-30 14:48:50
@misc{3430848,
  abstract     = {The day of bud burst (DBB) of different tree species are known to be
affected by factors such as growing degree days and temperature. In this
paper a two state Markov chain is used to model DBB for birch. The model
is fit using logistic regression and LASSO regularization is used to
evaluate which of many potential factors best forecast DBB. Data of birch
from both Finland and the United Kingdom is studied and differences
between the models adapted to the two countries are investigated. For
modeling purposes to capture the environment of forecasting, estimated
interpolated gridded climate data was used and not directly measured
climate data.
It is found that the models give very accurate predictions on the DBB. For
Finland it is little more than 2 days in mean absolute error (MAE). The
model is also fairly compact having less than 10 explaining covariates.
The covariate, accumulated growing degree days, was as expected part of
the models as well as among others variation of precipitation.},
  author       = {Burström, Jesse},
  language     = {eng},
  note         = {Student Paper},
  title        = {Bud burst of birch in Finland and the United Kingdom - Logistic regression analysis and modeling},
  year         = {2013},
}