It is now evident that the estimation of logistic regression parameters, using Maximum LikelihoodEstimator (MLE), suffers a huge drawback in the presence of outliers. An alternative approach is touse robust logistic regression estimators, such as Mallows type leverage dependent weights estimator(MALLOWS), Conditionally Unbiased Bounded Influence Function estimator (CUBIF), Bianco andYohai estimator (BY), and Weighted Bianco and Yohai estimator (WBY). This paper investigates therobustness of the preceding robust estimators by using real data sets and Monte Carlo simulations. Theresults indicate that the MLE behaves poorly in the presence of outliers. On the other hand, the WBYestimator is more efficient than the other existing robust estimators. Thus, it is suggested that the WBYestimator be employed when outliers are present in the data to obtain a reliable estimate.
In developing countries, motorcycle use has grown in popularity in the past decades. Commensurate with this growth is the increase in death and casualties among motorcyclists in these countries. One of the strategic programs to minimize this problem is to reduce motorcyclists exposure by shifting them into safer modes of transport. This study aims to explore the differences in the characteristics of bus and motorcycle users. It identifies the factors contributing to their choice of transport mode and estimates the probability that motorcyclists might change their travel mode to a safer alternative; namely, bus travel.