2]
Linear vs non-linear models
A statistical model is linear with respect to a parameter
if an estimate of that parameter can be obtained in a single
iteration. More...
3]
Parameterization
There may be more than one way to express a particular deterministic
model (i.e., functional relationship). More...
4]
Error specification
The error specification of a stochastic model is equal in
importance to the deterministic formulation. More...
5]
Maximum-likelihood
Most statistical models drawing conclusions from data utilize
the principle of maximum-likelihood as their basis for statistical
inference. More...
6]
Parameter estimation
Linear models can be solved for explicit analytical expressions
of the maximum-likelihood parameter estimates, given a model
and data. More...
7]
Diagnostics
A statistical model should be rejected if it fails a posteriori
goodness-of-fit diagnostics. More...
8]
Parsimony
Models with more free parameters typically 'fit' the data
better, i.e., model error variance is reduced, but at a cost
that has to be considered. More...
9]
Uncertainty
A underlying premise of stochastic models is that exact outcomes
cannot be predicted with certainty. More...
10]
Bias
Typically, maximum-likelihood estimates are asymptotically
unbiased. More...
12]
Bayesian models
For many biological considerations, the concept of an underlying
'true', point, value for a parameter as a premise for statistical
inference is somewhat artificial. More...
