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Density Sharpening: Principles and Applications to Discrete Data Analysis

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  • Subhadeep Mukhopadhyay

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.

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  • Subhadeep Mukhopadhyay, 2021. "Density Sharpening: Principles and Applications to Discrete Data Analysis," Papers 2108.07372, arXiv.org, revised Aug 2021.
    • Handle: RePEc:arx:papers:2108.07372
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    File URL: http://arxiv.org/pdf/2108.07372
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    References listed on IDEAS

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    1. Best, D. J. & Rayner, J. C. W., 1999. "Goodness of fit for the Poisson distribution," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 259-265, September.
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    Cited by:

    1. Subhadeep & Mukhopadhyay, 2022. "Modelplasticity and Abductive Decision Making," Papers 2203.03040, arXiv.org, revised Mar 2023.
    2. Subhadeep Mukhopadhyay, 2023. "Modelplasticity and abductive decision making," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 255-276, June.

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