  General purpose optimization routines such as nlminb, optim (R) or nlmixed
(SAS) are frequently used to estimate model parameters in nonstandard
distributions. This paper presents Particle Swarm Optimization (PSO), as an
alternative to many of the current algorithms used in statistics. We find that
PSO can not only reproduce the same results as the above routines, it can also
produce results that are more optimal or when others cannot converge. In the
latter case, it can also identify the source of the problem or problems. We
highlight advantages of using PSO using four examples, where: (1) some
parameters in a generalized distribution are unidentified using PSO when it is
not apparent or computationally manifested using routines in R or SAS; (2) PSO
can produce estimation results for the log-binomial regressions when current
routines may not; (3) PSO provides flexibility in the link function for
binomial regression with LASSO penalty, which is unsupported by standard
packages like GLM and GENMOD in Stata and SAS, respectively, and (4) PSO
provides superior MLE estimates for an EE-IW distribution compared with those
from the traditional statistical methods that rely on moments.
