• Corpus ID: 237142629

Density Sharpening: Principles and Applications to Discrete Data Analysis

  title={Density Sharpening: Principles and Applications to Discrete Data Analysis},
  author={Subhadeep Mukhopadhyay},
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. 

A maximum entropy copula model for mixed data: representation, estimation and applications

A new nonparametric model of maximum-entropy (MaxEnt) copula density function is proposed, which offers the following advantages: (i) it is valid for mixed random vector, and (ii) it plays a unifying role in the understanding of a large class of statistical methods for mixed .

Modelplasticity and Abductive Decision Making

‘All models are wrong but some are useful’ (George Box 1979). But, how to find those useful ones starting from an imperfect model? How to make informed data-driven decisions equipped with an imperfect



A Poissonness Plot

Abstract A graphical technique, similar in spirit to probability plotting, can be used to judge whether a Poisson model is appropriate for an observed frequency distribution. This “Poissonness plot”

Statistical distributions of earthquake numbers: consequence of branching process

We discuss various statistical distributions of earthquake numbers. Previously we derived several discrete distributions to describe earthquake numbers for the branching model of earthquake

Checking the shape of discrete distributions

Techniques for examining or testing the distributional behavior of a sample of continuous data have received much attention in the statistical literature. Most commonly the question is whether the

Statistics for discovery

The question is discussed of why investigators in engineering and the physical sciences rarely use statistical methods. It is argued that statistics has in the past been overly influenced by the

Goodness of fit for the Poisson distribution

Minimax Estimation of Functionals of Discrete Distributions

The minimax rate-optimal mutual information estimator yielded by the framework leads to significant performance boosts over the Chow-Liu algorithm in learning graphical models and the practical advantages of the schemes for the estimation of entropy and mutual information.

Tests of Fit for the Geometric Distribution

Abstract This article gives power comparisons of some tests of fit for the Geometric distribution. These tests include a Chernoff–Lehmann X 2 test, some smooth tests, a Kolmogorov–Smirnov test, and

Quantile processes with statistical applications

A Preliminary Study of Quantile Processes A Weak Convergence of the Normed Sample Quantile Process Strong Approximations of the Normed Quantile Process Two Approaches to Constructing Simultaneous

Seismic hazard analysis application of methodology, results, and sensitivity studies. Volume 4

As part of the Site Specific Spectra Project, this report seeks to identify the sources of and minimize uncertainty in estimates of seismic hazards in the Eastern United States. Findings are being