Lecture 8, page 1
Lecture 8: Time series and
biology
=> Modelling approaches to cycling populations
Kendall, B. E., C. J. Briggs, W. W. Murdoch, P. Turchin, S. P. Ellner, E.
McCauley, R. M. Nisbet, and S. N. Wood. 1999. Why do populations
cycle? A synthes
Lecture 5, page 1
Lecture 5: Estimation of time
series
Outline of lesson 5 (chapter 4)
(Extended version of the book):
a.)
Model formulation
Explorative analyses
Model formulation
b.)
Model estimation
Identification of order
Estimation of parameters
c.)
M
Lecture 10, page 1
Lecture 10: Bayesian
modelling of time series
Outline of lecture 10
What is Bayesian statistics?
What is a state-space model? Or why use Bayesian statistics?
Known vs. unknown distributions: BTS vs. BUGS
What is simulation?
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C:\Kyr
Lecture 9, page 1
Lecture 9: Nonparametric
modelling
What is the problem of parametric models?
We have no reason to believe the assumptions (e.g., linearity,
normality, stationarity etc.)
We have no reason to chose a specific model
In reality:
We disco
# R as a calculator
# You may use R as a calculator. For example:
3+2
3-2
3*2
3/2
3^2
sqrt(2)
exp(2)
log(2)
#Scalars
# You may define scalar variables and use them in computations. For example:
a = 2 # alternatively a<-2
b = 3 # alternatively b<-3
a+b
a*b
Lecture 3-4, page 1
Lecture 3-4: Probability
models for time series
Outline of lesson 3-4 (chapter 3)
The most heavy (and theoretical) part (and
lesson)
AR(p) processes
MA(q) processes
ARMA(p,q) processes
Linear process representation
ARIMA processes
Lecture 6, page 1
Lecture 6: Forecasting of time
series
Outline of lesson 6 (chapter 5)
Forecasting
Very complicated and untidy in the book
A lot of theory developed (in which we will not dwell).
Only chapter 5.2.4
Sketch of the theory
Practical example
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Lecture 7, page 1
Lecture 7: Estimation in the
frequency domain
A field with a really complex theory, that ends up with a very useful
exploratory tool.
Idea:
Decompose cfw_Xt into a sum of sinusoidal components with
uncorrelated random coefficients
1 n