(Un)Conditional Sample Generation Based on Distribution Element Trees. Issue 4 (2nd October 2018)

• Record Type: Journal Article
• Authors: Meyer, Daniel W.
• Is Part Of: Journal of computational and graphical statistics. Volume 27:Issue 4(2018)
• Page Start: 940
• Page End: 946
• Publication Date: 2018-10-02
• Subjects: Big data -- Bootstrap -- Sampling; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2018.1482768
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: Recently, distribution element trees (DETs) were introduced as an accurate and computationally efficient method for density estimation. In this work, we demonstrate that the DET formulation promotes an easy and inexpensive way to generate random samples similar to a smooth bootstrap. These samples can be generated unconditionally, but also, without further complications, conditionally using available information about certain probability-space components. This article is accompanied by the R codes that were used to produce all simulation results. Supplementary material for this article is available online.