scholarly journals ON INTERVAL ESTIMATION OF THE POISSON PARAMETER IN A ZERO-TRUNCATED POISSON DISTRIBUTION

2012 ◽  
Vol 25 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Kasumi Daidoji ◽  
Manabu Iwasaki
Keyword(s):  
Poisson Distribution ◽  
Interval Estimation ◽  
Poisson Parameter

scholarly journals The Fraction of Subspaces of $\mathrm{GF}(q)^n$ with a Specified Number of Minimal Weight Vectors is Asymptotically Poisson

10.37236/1288 ◽  
1996 ◽  
Vol 4 (1) ◽  
Author(s):  
Edward A. Bender ◽  
E. Rodney Canfield
Keyword(s):  
Vector Space ◽  
Poisson Distribution ◽  
Minimum Weight ◽  
Minimal Weight ◽  
Finite Vector ◽  
Finite Vector Space ◽  
Poisson Parameter

The weight of a vector in the finite vector space $\mathrm{GF}(q)^n$ is the number of nonzero components it contains. We show that for a certain range of parameters $(n,j,k,w)$ the number of $k$-dimensional subspaces having $j(q-1)$ vectors of minimum weight $w$ has asymptotically a Poisson distribution with parameter $\lambda={n\choose w}(q-1)^{w-1}q^{k-n}$. As the Poisson parameter grows, the distribution becomes normal.

scholarly journals A Note On the Estimation of the Poisson Parameter

1985 ◽  
Vol 8 (1) ◽  
pp. 193-196
Author(s):  
S. S. Chitgopekar
Keyword(s):  
Maximum Likelihood ◽  
Poisson Distribution ◽  
Functional Relationship ◽  
The Mean ◽  
Error Probabilities ◽  
Moments Estimates ◽  
Poisson Parameter

This paper considers the problem of estimating the mean of a Poisson distribution when there are errors in observing the zeros and ones and obtains both the maximum likelihood and moments estimates of the Poisson mean and the error probabilities. It is interesting to note that either method fails to give unique estimates of these parameters unless the error probabilities are functionally related. However, it is equally interesting to observe that the estimate of the Poisson mean does not depend on the functional relationship between the error probabilities.

scholarly journals Computational Simulations of Similar Probabilistic Distributions to the Binomial and Poisson Distributions

2020 ◽  
Author(s):  
Terman Frometa-Castillo ◽  
Anil Pyakuryal ◽  
Amadeo Wals-Zurita ◽  
Asghar Mesbahi
Keyword(s):  
Poisson Distribution ◽  
Statistical Models ◽  
Success Probability ◽  
Discrete Variable ◽  
Computational Simulations ◽  
Maximum Probability ◽  
Upper Limit ◽  
Probabilistic Distribution ◽  
Poisson Distributions ◽  
Poisson Parameter

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these legendary distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of the random discrete variable X. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

scholarly journals Computational Simulations of Similar Probabilistic Distributions to the Binomial and Poisson Distributions

2020 ◽  
Author(s):  
Terman Frometa-Castillo ◽  
Anil Pyakuryal ◽  
Amadeo Wals-Zurita ◽  
Asghar Mesbahi
Keyword(s):  
Poisson Distribution ◽  
Statistical Models ◽  
Success Probability ◽  
Discrete Variable ◽  
Computational Simulations ◽  
Maximum Probability ◽  
Upper Limit ◽  
Probabilistic Distribution ◽  
Poisson Distributions ◽  
Poisson Parameter

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these popular distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is the upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of a random discrete variable. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

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