Model-Checking Techniques Based on Cumulative Residuals
Residuals have long been used for graphical and numerical examinations of the adequacy of regression models. Conventional residual analysis based on the plots of raw residuals or their smoothed curves is highly subjective, whereas most numerical goodness-of-fit tests provide little information about the nature of model misspecification. In this paper, we develop objective and informative model-checking techniques by taking the cumulative sums of residuals over certain coordinates (e.g., covariates or fitted values) or by considering some related aggregates of residuals, such as moving sums and moving averages. For a variety of statistical models and data structures, including generalized linear models with independent or dependent observations, the distributions of these stochastic processes under the assumed model can be approximated by the distributions of certain zero-mean Gaussian processes whose realizations can be easily generated by computer simulation. Each observed process can then be compared, both graphically and numerically, with a number of realizations from the Gaussian process. Such comparisons enable one to assess objectively whether a trend seen in a residual plot reflects model misspecification or natural variation. The proposed techniques are particularly useful in checking the functional form of a covariate and the link function. Illustrations with several medical studies are provided.
Document Type: Research Article
Affiliations: 1: Department of Biostatistics, University of North Carolina, CB 7420 McGavran-Greenberg, Chapel Hill, North Carolina 27599-7420, U.S.A. 2: Department of Biostatistics, Harvard University, 677 Huntington Avenue, Boston, Massachusetts 02115, U.S.A. 3: Department of Statistics, 618 Mathematics, Columbia University, New York, New York 10027, U.S.A.
Publication date: March 1, 2002