  Generalized linear models are widely used in many areas of knowledge. As in
other classes of regression models, it is desirable to perform diagnostic
analysis in generalized linear models using residuals that are approximately
standard normally distributed. Diagnostic analysis in this class of models are
usually performed using the standardized Pearson residual or the standardized
deviance residual. The former has skewed distribution and the latter has
negative mean, specially when the variance of the response variable is high. In
this work, we introduce the adjusted quantile residual for generalized linear
models. Using Monte Carlo simulation techniques and two applications, we
compare this residual with the standardized Pearson residual, the standardized
deviance residual and two other residuals. Overall, the results suggest that
the adjusted quantile residual is a better tool for diagnostic analysis in
generalized linear models.
