  We present a method for distance calibration without using standard fitting
procedures. Instead we use random resampling to reconstruct the probability
density function (PDF) of calibration data points in the fitting plane. The
resulting PDF is then used to estimate distance-related properties. The method
is applied to samples of radio surface brightness to diameter (\Sigma-D) data
for the Galactic supernova remnants (SNRs) and planetary nebulae (PNe), and
period-luminosity (PL) data for the Large Magellanic Cloud (LMC) fundamental
mode classical Cepheids. We argue that resulting density maps can provide more
accurate and more reliable calibrations than those obtained by standard linear
fitting procedures. For the selected sample of the Galactic SNRs, the presented
PDF method of distance calibration results in a smaller average distance
fractional error of up to $\approx 16$ percentage points. Similarly, the
fractional error is smaller for up to $\approx 8$ and $\approx 0.5$ percentage
points, for the samples of Galactic PNe and LMC Cepheids, respectively. In
addition, we provide a PDF-based calibration data for each of the samples.
