degrossData: Creates a degrossData.object from the observed tabulated frequencies and central moments.

Description

Creates a degrossData.object from the observed tabulated frequencies and central moments.

Usage

degrossData(Big.bins, freq.j, m.j, I=300, K=25)

Arguments

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 of the J big bins.

The number of small bins used for quadrature during the normalization of the density during its estimation. Default: 300.

The desired number of B-splines in the basis used for density estimation. Default= 25.

Value

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.

References

Lambert, P. (2021) Moment-based density and risk estimation from grouped summary statistics. arXiv:2107.03883.

Examples

Run this code

# NOT RUN {
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)

# }