Influence diagnostics for the Gamma-Pareto regression: a DFFITS-based comparison of residuals
نویسندگان
1 Department of Statistics, Bahauddin Zakariya university, Multan Pakistan.
2 Department of Statistics, Bahauddin Zakariya University (BZU), Multan, Pakistan,60800
3 Department of Statistics Bahauddin Zakariya University (BZU), Multan, Pakistan,60800
doi
10.22034/jirss.2026.2076260.1154چکیده
The generalized linear models (GLMs) use Gamma-Pareto regression Model (G-PRM) to address the sensitivity of influential observations. Difference of Fits (DFFITS) is a popular technique for identifying influential observations. We apply DFFITS to the G-PRM with various residuals. We present illustrative real and simulated data. One class of adjusted Pearson residuals is more effective in detecting influential observations, considering small or large dispersion parameters. We calculate detection percentages to evaluate the proposed procedure's performance, replicating the process 10,000 times.The generalized linear models (GLMs) use Gamma-Pareto regression Model (G-PRM) to address the sensitivity of influential observations. Difference of Fits (DFFITS) is a popular technique for identifying influential observations. We apply DFFITS to the G-PRM with various residuals. We present illustrative real and simulated data. One class of adjusted Pearson residuals is more effective in detecting influential observations, considering small or large dispersion parameters. We calculate detection percentages to evaluate the proposed procedure's performance, replicating the process 10,000 times.
