Abstract:
Binary data is a common response data in many elds of research including
nance, social sciences, psychology and medicine. The most common model used
for the analysis of binary data is the logistic regression model. However, the problem
of identi cation and corresponding treatment of in uential outliers still remains to
be well studied to check the adequacy of the tted binary logistic models. Many
researchers have developed robust statistical model to solve this problem related to
the presence of atypical observations in the data. Gelman (2004) proposed a model
that dealt with outliers problem by trimming the probability of success in logistic
regression. The trimming values in this model are xed and the user is required to
specify this value well in advance. We explore this work and other robust logistic
regression models then extend this work to allow for the trimming value to be
estimated from the data. In particular, this research work presents a self selecting
robust logistic regression (SsRLR) model. We proved that the SsRLR model is more
robust to the presence of leverage points in the data. Parameter estimations is done
using a full Bayesian approach, implemented in WinBUGS 14 software.
