A statistical model of random numbers
data(normal)A list object named `qsd` of class QLmodel with additional elements
simfn simulation function
sim simulation results at design points, class `simQL`
OPT result from call to estimation function qle
QS quasi-scoring iteration results after initial approximation
This is a pedagogic example of a simulated data set for quasi-likelihood estimation using
normally distributed random numbers. The model outcome is a vector of summary statistics, that is,
simply the median and mean average deviation of n=10 random numbers, which is evaluated at the
model parameter \(\theta=(\mu,\sigma)\) with mean \(\mu\) and standard deviation \(\sigma\) as
the parameters of the normal distribution. We estimate the model parameter given a specific
"observation" of those summary statistics. Clearly, maximum likelihood estimation would be the
method of first choice if we had a real sample of observations. However, this example is used to demonstrate
the basic workflow of estimating the model parameter. We use this model as a standard example in the package
documentation.