Robust estimations in Poisson regression model

Many regression estimation techniques are strongly affected by outlier data and many errors occur in their estimation. In the recent years, robust methods have been developed to solve this issue. The minimum density power divergence estimator is an estimation method based on the minimum distance between two density functions, which provides a robust estimate in situations where the data contain a number of outliers. In this research, we present the robust estimation method of minimum density power divergence to estimate the parameters of the Poisson regression model, which can produce robust estimators with the least loss in efficiency. Also, we will investigate the performance of the proposed estimators by providing a real example.