Density Sharpening: Principles and Applications to Discrete Data Analysis
Methodology
2021-08-24 v3 Econometrics
Statistics Theory
Applications
Statistics Theory
Abstract
This article introduces a general statistical modeling principle called "Density Sharpening" and applies it to the analysis of discrete count data. The underlying foundation is based on a new theory of nonparametric approximation and smoothing methods for discrete distributions which play a useful role in explaining and uniting a large class of applied statistical methods. The proposed modeling framework is illustrated using several real applications, from seismology to healthcare to physics.
Keywords
Cite
@article{arxiv.2108.07372,
title = {Density Sharpening: Principles and Applications to Discrete Data Analysis},
author = {Subhadeep Mukhopadhyay},
journal= {arXiv preprint arXiv:2108.07372},
year = {2021}
}
Comments
Keywords: Density sharpening principle; LP-Fourier analysis; Explanatory goodness-of-fit; Jaynes' dice problem; Compressive chi-square; Data-efficient learning