New generic implementation of multiple regression analysis under noisy measurements.
Maximum likelihood regression in a mixed-effect linear model using the EM algorithm.
Parameters : | Y : array
VY : array C is the contrast matrix. Conventionally, C is p x q where p : is the number of regressors. : OUTPUT: beta, s2 : beta – array of parameter estimates : s2 – array of squared scale parameters. : REFERENCE: : Keller and Roche, ISBI 2008. : |
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Log-likelihood ratio statistic: 2*(log L - log L0)
It is asymptotically distributed like a chi-square with rank(C) degrees of freedom under the null hypothesis H0: Cb = 0.
Force strictly positive values.