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MaskDensity14: an R package for the density approximant of a univariate based on noise multiplied data

Journal Article


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Abstract


  • 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.

Authors


  •   Lin, Yan-Xia
  •   Fielding, Mark James. (external author)

Publication Date


  • 2015

Citation


  • Lin, Y. & Fielding, M. James. (2015). MaskDensity14: an R package for the density approximant of a univariate based on noise multiplied data. SoftwareX, 3-4 37-43.

Scopus Eid


  • 2-s2.0-84949235023

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6336&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5308

Number Of Pages


  • 6

Start Page


  • 37

End Page


  • 43

Volume


  • 3-4

Abstract


  • 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.

Authors


  •   Lin, Yan-Xia
  •   Fielding, Mark James. (external author)

Publication Date


  • 2015

Citation


  • Lin, Y. & Fielding, M. James. (2015). MaskDensity14: an R package for the density approximant of a univariate based on noise multiplied data. SoftwareX, 3-4 37-43.

Scopus Eid


  • 2-s2.0-84949235023

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6336&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5308

Number Of Pages


  • 6

Start Page


  • 37

End Page


  • 43

Volume


  • 3-4