Regression models with an increasing number of unknown parameters and with unknown and different variances of observation error are of interest in important applications. The reason for this is that, with an increased number of unknown parameters, the unknown function can be approximated more accurately in experiments. Moreover, in some applications, repeated tests at a single point are costly (financially and technically), which hampers the estimation of the unknown error variance, which is different at different observation points.