This function creates bootstrap samples of input data and fits a dynamic mixture via
maximum likelihood.
Usage
MLEBoot(x, y, intTol, weight)
Value
A list with the following elements:
MLE: maximum likelihood estimates obtained from each bootstrap sample.
errors: number of times the MLE algorithm breaks down.
Arguments
x
list of integers: indices of replications.
y
numerical vector: observed data.
intTol
threshold for stopping the computation of the integral in the normalization
constant: if the integral on the interval from n-1 to n is smaller than intTol, the approximation procedure stops.
weight
'cau' or 'exp': name of weight distribution.
Details
MLEs are computed by means of the optim function. When it breaks
down, the sample is discarded and a new one is generated. The function keeps
track of the number of times this happens.