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In many applied fields incomplete covariate vectors are commonly encountered.It is well known that this can be problematic when making inference on modelparameters, but its impact on prediction performance is less understood. Wedevelop a method based on covariate dependent partition models that seamlesslyhandles missing covariates while completely avoiding any type of imputation.The method we develop allows in-sample predictions as well as out-of-sampleprediction, even if the missing pattern in the new subjects' incompletecovariate vector was not seen in the training data. Any data type, includingcategorical or continuous covariates are permitted. In simulation studies theproposed method compares favorably. We illustrate the method in two applicationexamples.

Clustering and Prediction with Variable Dimension Covariates