estimation offers to find a minimum of the functional:

$$\sum_{l,m} \ln(W_l^2 \langle(a_l(Q,n))^2 \rangle + \langle (a_{l,noise}^m)^2
\rangle ) + \frac{(b_l^m)^2}{W_l^2 \langle(a_l(Q,n))^2 \rangle +
\langle (a_{l,noise}^m)^2 \rangle}
\label{likhood}$$

relative $Q$ and $n$ with given spectrum shape and measured signal and noise sample spectra. Here $\langle(a_l(Q,n))^2 \rangle$ is the model signal spectrum, depending upon desired parameters and described as (\[approx\_DTT\]), $(b_l^m)^2$ is the measured spectrum (we describe it further), and $\langle(a_{l,noise}^m)^2 \rangle$ is a model noise spectrum (\[sp\_noise\]), its amplitude is found using the same approach but from the difference map .

In addition we are to take into account a procedure of the Galactic radiation “cleaning” and a procedure of a conversion the antenna temperatures into a thermodynamic scale. In all cases the summation is taken over multipoles chosen for the analysis. The necessary conditions for the described procedure is a knowledge of the measured signal and noise spectra.

 COBE Data Noise Analysis.
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There are a lot of papers concerning the COBE data noise analysis. Bennett et al., 1994, Lineweaver et al., 1994 discussed the correlation noise properties and showed a weak ( 0.45% ) correlation at 60$^\circ$. So in practice we may assume the noise is uncorrelated. The noise 