Kernelheaping: Kernel Density Estimation for Heaped Data

In Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data

Description

In self-reported or anonymized 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. Additionally, bivariate non-parametric density estimation for rounded data is supported.

Details

The most important function is dheaping. See the help and the attached examples on how to use the package.

Kernelheaping documentation built on Jan. 27, 2022, 1:09 a.m.