Description Usage Arguments Value Author(s) References See Also Examples
Creates a degrossData.object from the observed tabulated frequencies and central moments.
1 | degrossData(Big.bins, freq.j, m.j, I=300, K=25)
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Big.bins |
Vector of length |
freq.j |
The number of data observed within each big bin. |
m.j |
A matrix of dim |
I |
The number of small bins used for quadrature during the normalization of the density during its estimation. Default: |
K |
The desired number of B-splines in the basis used for density estimation. Default= |
A degrossData.object, i.e. a list containing:
small.bins : a vector of length I+1 with the small bin limits.
ui : the I midpoints of the small bins.
delta : width of the small bins.
I : the number of small bins.
B.i : a matrix of dim I by K with the B-spline basis evaluated at the small bin midpoints.
K : number of B-splines in the basis.
knots : equidistant knots supporting the B-splines basis.
Big.bins : vector of length J+1 with the limits of the J big bins containing the data used to produce the tabulated statistics.
freq.j : the number of data observed within each big bin.
m.j : a matrix of dim J by 4 giving the first 4 sample central moments within each big bin.
J : the number of big bins.
small.to.big : a vector of length I indicating to what big bin each element of ui belongs.
Philippe Lambert p.lambert@uliege.be
Lambert, P. (2021) Moment-based density and risk estimation from grouped summary statistics. arXiv:2107.03883.
1 2 3 | sim = simDegrossData(n=3500, plotting=TRUE)
obj.data = degrossData(Big.bins=sim$Big.bins, freq.j=sim$freq.j, m.j=sim$m.j)
print(obj.data)
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