protected override Vector<double> ComputeConsequentialParameters(Vector<double> parameter)
{
// fill in mean and sigma
Vector<double> pbak = Parameters;
Parameters = parameter;
Vector<double> newParms;
double[] rs;
double[] forecs;
double[] res;
if (!DataIsLongitudinal())
{
// case 1: standard univariate time series data
if (ParameterStates[0] == ParameterState.Consequential)
Mu = values.SampleMean();
if (ParameterStates[1] == ParameterState.Consequential)
{
autocovariance = ComputeACF(values.Count + 1, false);
var adjusted = ComputeAdjustedValues(); // account for exogenous inputs
res = ComputeSpecialResiduals(adjusted, out rs, 0, out forecs);
if (TailDegreesOfFreedom == 0) // i.e. if innovations are normal
{
// consequential sigma is just the standard deviation
double ss = 0;
for (int i = 0; i < res.Length; ++i)
ss += res[i] * res[i];
Sigma = Math.Sqrt(ss / res.Length);
}
else // for model with t-distribution innovations
{
var tdn = new StudentT(0, 1, TailDegreesOfFreedom);
var vres = Vector<double>.Build.Dense(res);
Sigma = tdn.MLEofSigma(vres);
}
}
newParms = Vector<double>.Build.DenseOfVector(Parameters);
}
else
{
// case 2: longitudinal data
throw new ApplicationException("Longitudinal data not yet supported with ARMAX models");
}
Parameters = pbak;
return newParms;
}
