Diagnostics via partial residual plots in inverse Gaussian regression

Regression diagnostics is the basic requirement to apply regression analysis to reach reliable conclusions. Generalized linear models also required diagnostics for its implementation. The construction of partial residuals using response residuals for the inverse Gaussian regression model is carried out to explore the structure and usefulness for visualizing diagnostics and curvature as a function of selected predictors. The current study established the performance of partial residual plots over conventional diagnostic methods. The comparison has been made using aerial biomass data and with the help of simulation study. It has been observed that partial residual plots provide much better diagnosis than do conventional methods. Moreover, multiple diagnostics in a single display provide better perceptive towards lack of fit, specification, and data anomalies. Diagnostics via Partial Residual Plots in Inverse Gaussian Regression In this article, we constructed the partial residuals for inverse Gaussian regression model as a function of covariates to assess the structure of model and its usefulness in diagnostics. The current study established the performance of partial residual plots over conventional diagnostic methods. The comparison has been made using real and simulated data and it has been observed that partial residual plots provide much better diagnosis as compared to conventional methods.