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)
|
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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