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[[Multivariate function]]s can be constructed using multiplicative model estimators. Where a matrix function of {{math|1=''A''}} is defined as <math display="block">D_N = N^2 \times N(N + 1) / 2</math>
A sum can be [[logarithmic distribution|distributed]] across the product<math display="block">Y_t = \sum(t/T)^{1/2}u_t = \sum(t/T)^{1/2}G_t^{1/2}\epsilon_t</math>
For the efficient estimation of {{math|1=Σ(.)}}, the following two [[nonparametric regression]]s can be considered: <math display="block">\tilde{y}^2_t = \frac{y^2_t}{g_t} = \sigma^2(t/T) + \sigma^2(t/T)(\epsilon^2_t - 1)</math>
and <math display="block">y^2_t = \sigma^2(t/T) + \sigma^2(t/T)(g_t\epsilon^2_t - 1)</math>
==See also==
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