simDegrossData: Simulation of grouped data and their sample moments to...
In degross: Density Estimation from GROuped Summary Statistics

Description Usage Arguments Value Author(s) References See Also Examples

Simulation of grouped data and their sample moments to illustrate the degross density estimation procedure

1	simDegrossData(n, plotting=TRUE, choice=2, J=3)

`n`	Desired sample size
`plotting`	Logical indicating whether the histogram of the simulated data should be plotted. Default: FALSE
`choice`	Integer in 1:3 indicating from which mixture of distributions to generate the data
`J`	Number of big bins

A list containing tabulated frequencies and central moments of degrees 1 to 4 for data generated using a mixture density. This list contains :

n : total sample size.
J : number of big bins.
Big.bins : vector of length J+1 with the big bin limits.
freq.j : vector of length J with the observed big bin frequencies.
m.j : J by 4 matrix with on each row the observed first four sample central moments within a given big bin.
true.density : density of the raw data generating mechanism (to be estimated from the observed grouped data).
true.cdf : cdf of the raw data generating mechanism (to be estimated from the observed grouped data).

Philippe Lambert p.lambert@uliege.be

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

degrossData.

## Generate data
sim = simDegrossData(n=3500, plotting=TRUE, choice=2, J=3)
print(sim$true.density) ## Display density of the data generating mechanism

# Create a degrossData object
obj.data = with(sim, degrossData(Big.bins=Big.bins, freq.j=freq.j, m.j=m.j))
print(obj.data)