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We collect robust proposals given in the field of regression models withheteroscedastic errors. Our motivation stems from the fact that thepractitioner frequently faces the confluence of two phenomena in the context ofdata analysis: non--linearity and heteroscedasticity. The impact ofheteroscedasticity on the precision of the estimators is well--known, howeverthe conjunction of these two phenomena makes handling outliers more difficult.  An iterative procedure to estimate the parameters of a heteroscedasticnon--linear model is considered. The studied estimators combine weighted$MM-$regression estimators, to control the impact of high leverage points, anda robust method to estimate the parameters of the variance function.

Robust estimation of heteroscedastic regression models: a brief overview and new proposals