Stochastic Covariates in Poisson Regression

Music City Center 

Analyzing environmental data can be challenging when making predictions due to outliers and other irregularities in the data. Large environmental datasets often contain measurements that deviate from the norm, and these outliers can significantly distort traditional analyses, potentially leading to biased or invalid results. As a result, identifying and addressing outliers is essential. Robust methods can produce reliable results even when the data has skewed, heavy-tailed, or non-normal distributions. These methods provide dependable parameter estimates despite the presence of anomalies, leading to more trustworthy conclusions and decisions.
In this study, we assume that covariates in a Poisson regression model are non-stochastic, which allows for the inclusion of non-normality and extreme values in the model's systematic component, as commonly found in environmental data. We propose a novel estimation method and compare the performance of our proposed estimators with traditional techniques, demonstrating that the new estimators are indeed robust. Finally, we apply these estimators to a real-life dataset.

Outliers

Robustness

Poisson Regression

Stochastic covariates 

Biometrics Section