simDegrossData {degross}

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

Description

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

Usage

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

Arguments

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

Value

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).

Author(s)

Philippe Lambert [e-mail]

References

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

See Also

degrossData.

Examples

## 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)