StandardWeightedCubicQuantileFitInclEnds
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Function Description
Returns an array corresponding to a weighted cubic curve fit
to observed quantile values, where the weights correspond to the distance
between consecutive points (so that the fit gives greater weight to the tails
of the distribution). Requires as inputs an ordered array of 
values
(e.g. expected quantiles, as per MnStandardisedNormalQuantiles
and an ordered array of 
values
corresponding to the actually observed values.
The curve fit is derived using the Nematrian least squares
generalised curve fit MnLeastSquaresGeneralisedCurveFit,
(specifically the polynomial
version).
The weights, 
,
used in this function, if the ordered -values
are 
and
there are 
such
values, are:
An alternative that gives zero weight to the two end points
is MnStandardWeightedCubicQuantileFit.
Please bear in mind that a cubic is not always a valid form
for a quantile-quantile function to take (e.g. if the slope of the cubic
becomes negative anywhere). If you wish to avoid this problem then we suggest
fitting a distributional form using e.g. (weighted) maximum likelihood, see
e.g. MnProbDistWeightedMLE.
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Output type / Parameter details
Output type: Double()
| Parameter Name | Variable Type | Description |
| OrderedXvals | Double() | Array containing x's (ordered) |
| OrderedYvals | Double() | Array containing y's (ordered) |
Links to:
-
Interactively run function
-
Interactive instructions
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Example calculation
-
Output type / Parameter details
-
Illustrative spreadsheet
-
Other Statistical functions
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Computation units used
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