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However, in the situation of unreplicated designs, most methods known so far rely on the assumption of constant variances, or a functional relationship between mean and variance as the only source of heteroscedasticity.
The test for homogeneity of variance was conducted using the Breusch-Pagan/Cook-Weisberg test for heteroskedasticity, and the hypothesis of constant variances of the residuals was not rejected (p > 0.25) in both equations.
All of these random coefficients were assumed to be normally distributed with the mean of 0 and some constant variances.
Constant variances are assumed for α and { β m } in their respective prior specifications, whereas a common random variance σ is assumed for the error terms.
The result is a least-squares estimator as follows: (25) Assume constant variances across classes and, further, that PDQ's are known with certainty.
In our examples, however, we found no hints, that the assumption of constant variances might be violated, the respective empirical estimates were quite similar in all cases.
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random variables with mean zero and a constant variance.
(We refer to such models as constant variance models).
The method is appropriate for target data with non constant variance (or volatility).
We performed non-response bias before checking assumptions such as constant variance and normality.
u i is the usual i.i.d, zero mean regression error with constant variance.
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