summary.Kernelheaping

<p>Kernelheaping object produced by <code>dheaping</code> function</p>

object

<p>Prints some descriptive statistics (means and quantiles) for the estimated rounding, bias and acceleration (beta) parameters</p>

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.

Marcus Gross

Kernelheaping

summary.Kernelheaping: Prints some descriptive statistics (means and quantiles) for the estimated rounding, bias and acceleration (beta) parameters

Description

Usage

Arguments

Value