Maximum likelihood estimates
specifies options using name-value pair arguments in addition to any of the input arguments in
previous syntaxes. For example, you can specify the censored data, frequency of observations,
and confidence level.phat
= mle(___,Name,Value
)
When you supply distribution functions, mle
computes the parameter
estimates using an iterative maximization algorithm. With some models and data, a poor choice of
starting point can cause mle
to converge to a local optimum that is not the
global maximizer, or to fail to converge entirely. Even in cases for which the log-likelihood is
well-behaved near the global maximum, the choice of starting point is often crucial to
convergence of the algorithm. In particular, if the initial parameter values are far from the
MLEs, underflow in the distribution functions can lead to infinite log-likelihoods.