The name is absent

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choice for an approximating distribution is the empirical distribution of the observed
data. Bootstrapping can be used to calculate standard errors of parameter estimates.
Furthermore, confidence intervals of parameter estimates can be obtained. When the
original sample size is small, the bootstrapping method can be used to create more
samples in order to infer to the population. The bootstrap was developed by Efron
in the late 1970s.

In this research, the bootstrapping method is used to calculate the covariance
matrices of parameter estimates. See details in Section 3.4.

2.2.4 Sensitivity analysis

Sensitivity analysis is used to determine how “sensitive” a model is to changes in the
value of the parameters of the model or to changes in the structure of the model. In
this research, we conducted a sensitivity analysis on parameter sensitivity.

Parameter sensitivity is usually performed as a series of tests in which the modeler
sets different parameter values to see how a change in the parameter causes a change
in the dynamic behavior of the models’ outputs. By showing how the model behavior
responds to changes in parameter values, sensitivity analysis is a useful tool in model
building as well as in model evaluation. Sensitivity analysis helps build confidence in
the model by studying the uncertainties that are often associated with parameters in
models. See further details about how sensitivity analysis is used in this research in

Section 3.1.2.

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