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Pseudo-cumulative distribution function with applications
Soft Computing, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Agahi, Hamzeh, Mehri-Dehnavi, Hossein
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The Cumulative Distribution Function
1989All 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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Approximating a Cumulative Distribution Function by Generalized Hyperexponential Distributions
Probability in the Engineering and Informational Sciences, 1997Recent 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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Fuzzy Cumulative Distribution Function and its Properties
2020The 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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Cumulative distribution function of a geometric Poisson distribution
Journal of Statistical Computation and Simulation, 2008The 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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Deconvolution of Cumulative Distribution Function with Unknown Noise Distribution
Acta Applicandae Mathematicae, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On discretizations of cumulative distribution functions
Mathematische Nachrichten, 2011AbstractStochastic processes in discrete time are considered which develop through the successive application of independent positive multipliers and also are martingales. We construct optimal discretizations and derive properties of the Mellin‐Stieltjes transforms of the cumulative distribution functions of the multipliers.
Jensen, Gerd, Pommerenke, Christian
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The Binomial Cumulative Distribution Function
1989Let 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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The Poisson Cumulative Distribution Function
1989Let 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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On Secant Approximations to Cumulative Distribution Functions
Biometrika, 1993Summary: 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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