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The Cumulative Distribution Function

1989
All of the arithmetic associated with testing in the hyper-geometric distribution and with confidence intervals in the binomial distribution (respectively, Lessons 15, 19, Part I) was based on their CDFs. Here the extension of this latter concept will be made in two steps, one emphasizing the type of function which is “random”, the other emphasizing ...
Hung T. Nguyen, Gerald S. Rogers
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Cumulative distribution function of a geometric Poisson distribution

Journal of Statistical Computation and Simulation, 2008
The geometric Poisson distribution (also called Polya–Aeppli) is a particular case of the compound Poisson distribution. We propose to express the general term of this distribution through a recurrence formula leading to a linear algorithm for the computation of its cumulative distribution function.
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Graphing Cumulative Distribution Functions

Educational and Psychological Measurement, 2000
This article presents a method for producing graphical displays of cumulative distribution functions. Step-by-step instructions on how to produce these graphs are provided using a concrete example. By following the step-by-step instructions, researchers will be able to produce the plots for themselves using SPSS.
Jesus Tanguma, F. M. Speed
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On Secant Approximations to Cumulative Distribution Functions

Biometrika, 1993
Summary: We investigate the properties of an approximation, called the secant approximation, to the cumulative distribution function, where the density is of a broad parametric class. Formulae for higher order terms are derived that give the approximation explicitly in terms of functions that define the density.
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Approximating a Cumulative Distribution Function by Generalized Hyperexponential Distributions

Probability in the Engineering and Informational Sciences, 1997
Recent developments in stochastic modeling show that enormous analytical advantages can be gained if a general cumulative distribution function (c.d.f.) can be approximated by generalized hyperexponential distributions. In this paper, we introduce a procedure to explicitly construct such approximations of an arbitrary c.d.f.
Ou, J., Li, J., Özekici, S.
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Deconvolution of Cumulative Distribution Function with Unknown Noise Distribution

Acta Applicandae Mathematicae, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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The Poisson Cumulative Distribution Function

1989
Let X be a Poisson random variable characterized by the parameter μ. This table contains values of the Poisson cumulative distribution function\(F(x;\mu ) = p(X \le x) = \sum\limits_{y = 0}^x {{{{e^{ -\mu }}{\mu ^y}} \over {{y^!}}}} .\)
Stephen Kokoska, Christopher Nevison
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The Binomial Cumulative Distribution Function

1989
Let X be a binomial random variable characterized by the parameters n and p. This table contains values of the binomial cumulative distribution function\(B(x;n,p) = p(X \le x) = \sum\limits_{y = 0}^x {b(y;n,p) = \sum\limits_{y = 0}^x {(_y^n){p^y}{{(1 -p)}^{n -y.}}} } \)
Stephen Kokoska, Christopher Nevison
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Arbitrarily tight bounds on cumulative distribution function of Beckmann distribution

2017 International Conference on Computing, Networking and Communications (ICNC), 2017
Beckmann distribution is a versatile mathematical model, which can be applied in performance analyses of radio frequency communications, free-space optical communications and underwater optical communications. However, the cumulative distribution function (CDF) of Beckmann distribution does not have a closed-form expression, which makes it challenging ...
Bingcheng Zhu   +2 more
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Fuzzy Cumulative Distribution Function and its Properties

2020
The statistical methods based on cumulative distribution function is a start point for  many parametric or nonparametric statistical inferences. However, there are many practical problems that require dealing with observations/parameters that represent inherently imprecise.  However, Hesamian and Taheri (2013) was extended a concept of fuzzy cumulative
Hesamian, Gholamreza, Shams, Mehdi
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