The PDF for the exponential distribution is as follows: where: x is the random variable (typically representing time) λ is the rate parameter (λ > 0) e is Euler's number (approximately 2.71828) Cumulative Distribution Function (CDF) The CDF is particularly useful when we want to find ...
Estimation of the PDF and the CDF of exponentiated moment exponential distributionExponentiated moment exponential distributionLeast squares estimatorMaximum likelihood estimatorModel selection criteriaPercentile estimatorWeighted least squares estimatorThis article addresses the different methods of estimation of the...
The equation for the 2-parameter exponential cumulative density function, or cdf, is given by: Recalling that the reliability function of a distribution is simply one minus the cdf, the reliability function of the 2-parameter exponential distribution is given by: ...
distribution, called the exponentiated exponential distribution, defined by the cumulative distribution function (cdf) F -λx α-1 ( x;α,λ =(1-e )... GM Cordeiro,EMM Ortega,DCCD Cunha - 《Journal of Data Science》 被引量: 193发表: 2013年 Exponentiated Weibull–Poisson distribution: Mode...
On estimation of the PDF and the CDF of the one-parameter polynomial exponential family of distributionsLindley distributionmaximum likelihood estimatoruniformly minimum variance unbiased estimatorIn this article, we have considered the estimation of the probability density function and cumulative distribution ...
Estimation of the PDF and the CDF of the two-parameter exponential distribution for type-II censored sampleIndrani MukhrejeeSudhansu S. MaitiYazd University
The GE distribution was proposed and studied quite extensively by Gupta and Kundu [9], and has the cdf 𝐹𝐺𝐸(𝑥;𝛾,𝜃)=(1−𝑒−𝜃𝑥)𝛾,𝑥>0,FGE(x;γ,θ)=(1−e−θx)γ,x>0, and the pdf 𝑓𝐺𝐸(𝑥;𝛾,𝜃)=𝛾𝜃(1−𝑒−𝜃𝑥...
Recently, Poonia and Azad [1] studied a new exponentiated generalized linear exponential distribution (NEGLED) with cumulative distribution density function (CDF) given by F ( x ) = 1 − e − λ 1 2 x 2 + λ 2 x − λ 3 α β I ( φ , ∞ ) ( x ) , for λ 1 >...