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Maximum likelihood type robust estimates of regression are defined and their asymptotic properties are investigated both theoretically and empirically. Perhaps the most important new feature is that the number of parameters is allowed to increase with the number of observations. The initial terms of a formal power series expansion (essentially in powers of ) show an excellent agreement with Monte Carlo results, in most cases down to 4 observations per parameter.