from
Anderson-Darling Goodness Of Fit Test to Inverse Gaussian Distbtn
by Matthew Brenneman
Tests M random samples of N random vars to determine if they are from Inverse Gaussian distbtn.
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| Subr_ComputeCritVals(Shape,TableShape,TableCV,Alpha)
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function [CV] = Subr_ComputeCritVals(Shape,TableShape,TableCV,Alpha)
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global M
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CV = zeros(1,M); % Exact Critical Value Computed from Table Using Logarithmic Interpolation from Table
MaxShapeIndex = max(TableShape);
MinShapeIndex = min(TableShape);
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for ii = 1:M
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ShapeIndex = log2(Shape(1,ii));
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if (ShapeIndex > MinShapeIndex) && (ShapeIndex < MaxShapeIndex)
Index = sum(ShapeIndex > TableShape);
yl = TableCV(Index);
yu = TableCV(Index+1);
xl = TableShape(Index);
xu = TableShape(Index+1);
IntRatio = (yu - yl)/(xu-xl);
CV(1,ii) = yl + IntRatio*(ShapeIndex-xl);
else
if ShapeIndex < MinShapeIndex
CV(1,ii) = min(TableCV);
else
if Alpha == 10
CV(1,ii) = 0.63;
end
if Alpha == 5
CV(1,ii) = 0.76;
end
if Alpha == 1
CV(1,ii) = 1.04;
end
end
end
end
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