Kernel Density Estimation for Heaped and Rounded Data

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) (). Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (), as well as data aggregated on areas is supported.


Reference manual

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2.2.8 by Marcus Gross, 7 months ago

Browse source code at

Authors: Marcus Gross [aut, cre] , Kerstin Erfurth [ctb]

Documentation:   PDF Manual  

GPL-2 | GPL-3 license

Imports sp, plyr, fastmatch, fitdistrplus, GB2, magrittr, mvtnorm

Depends on MASS, ks, sparr

Suggested by smicd.

See at CRAN