Open AccessMaskDensity14: An R package for the density approximant of a univariate based on noise multiplied data
Author(s) -
YanXia Lin,
Mark James Fielding
Publication year - 2015
Publication title -
softwarex
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 21
ISSN - 2352-7110
DOI - 10.1016/j.softx.2015.11.002
Subject(s) - univariate , computer science , r package , probability density function , software , density estimation , sample (material) , moment (physics) , microdata (statistics) , data mining , algorithm , noise (video) , theoretical computer science , mathematics , statistics , artificial intelligence , computational science , multivariate statistics , machine learning , programming language , population , chemistry , physics , demography , chromatography , classical mechanics , estimator , sociology , image (mathematics) , census
Lin (2014) developed a framework of the method of the sample-moment-based density approximant, for estimating the probability density function of microdata based on noise multiplied data. Theoretically, it provides a promising method for data users in generating the synthetic data of the original data without accessing the original data; however, technical issues can cause problems implementing the method. In this paper, we describe a software package called MaskDensity14, written in the R language, that uses a computational approach to solve the technical issues and makes the method of the sample-moment-based density approximant feasible. MaskDensity14 has applications in many areas, such as sharing clinical trial data and survey data without releasing the original data
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