createSim.Kernelheaping

name of the distribution where random sampling is available, e.g. "norm"

distribution

rounds

thresholds

certain value added to all observed random samples

offset

downbias

Beta

additional attributes handed over to "rdistribution" (i.e. rnorm, rgamma,..)

In self-reported or anonymised data the user often encounters
heaped data, i.e. data which are rounded (to a possibly different degree
of coarseness). While this is mostly a minor problem in parametric density
estimation the bias can be very large for non-parametric methods such as kernel
density estimation. This package implements a partly Bayesian algorithm treating
the true unknown values as additional parameters and estimates the rounding
parameters to give a corrected kernel density estimate. It supports various
standard bandwidth selection methods. Varying rounding probabilities (depending
on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (<doi:10.1093/jssam/smw011>).
Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>),
as well as data aggregated on areas is supported.

createSim.Kernelheaping: Create heaped data for Simulation

Description

Usage

Arguments

Value