202. Note: Significance Factors for the Ratio of a Poisson Variable to Its Expectation

Biometrics ◽  
1964 ◽  
Vol 20 (3) ◽  
pp. 639 ◽  
Author(s):  
John C. Bailar III ◽  
Fred Ederer
Keyword(s):  
Poisson Variable

Prediction of Multimodal Poisson Variable using Discretization of Gaussian Data

2021 ◽  
Author(s):  
Evženie Uglickich ◽  
Ivan Nagy ◽  
Matej Petrouš
Keyword(s):  
Poisson Variable ◽  
Gaussian Data

Confidence Intervals for the Expectation of a Poisson Variable

Biometrika ◽  
1959 ◽  
Vol 46 (3/4) ◽  
pp. 441 ◽  
Author(s):  
Edwin L. Crow ◽  
Robert S. Gardner
Keyword(s):  
Confidence Intervals ◽  
Poisson Variable

scholarly journals On a transformation of the weighted compound Poisson process

1971 ◽  
Vol 6 (1) ◽  
pp. 42-46 ◽  
Author(s):  
Hans Bühlmann ◽  
Roberto Buzzi
Keyword(s):  
Poisson Process ◽  
Compound Poisson Process ◽  
Random Variable ◽  
Characteristic Functions ◽  
Compound Poisson ◽  
Common Distribution ◽  
Common Distribution Function ◽  
Poisson Variable ◽  
Image Position ◽  
Independent Identically Distributed

We are using the following terminology—essentially following Feller:a) Compound Poisson VariableThis is a random variable where X1, X2, … Xn, … independent, identically distributed (X0 = o) and N a Poisson counting variablehence(common) distribution function of the Xj with j ≠ 0 or in the language of characteristic functionsb) Weighted Compound Poisson VariableThis is a random variable Z obtained from a class of Compound Poisson Variables by weighting over λ with a weight function S(λ)henceor in the language of characteristic functionsLet [Z(t); t ≥ o] be a homogeneous Weighted Compound Poisson Process. The characteristic function at the time epoch t reads thenIt is most remarkable that in many instances φt(u) can be represented as a (non weighted) Compound Poisson Variable. Our main result is given as a theorem.

scholarly journals A Weighted Poisson Distribution for Underdispersed Count Data

2021 ◽  
Vol 10 (4) ◽  
pp. 157
Author(s):  
Chedly Gelin Louzayadio ◽  
Rodnellin Onesime Malouata ◽  
Michel Diafouka Koukouatikissa
Keyword(s):  
Poisson Distribution ◽  
Count Data ◽  
Exponential Family ◽  
Discrete Distribution ◽  
Random Variable ◽  
Poisson Random Variable ◽  
Weighted Version ◽  
Poisson Variable ◽  
Two Parameter ◽  
New Distribution

In this paper, we present a new weighted Poisson distribution for modeling underdispersed count data. Weighted Poisson distribution occurs naturally in contexts where the probability that a particular observation of Poisson variable enters the sample gets multiplied by some non-negative weight function. Suppose a realization y of Y a Poisson random variable enters the investigator’s record with probability proportional to w(y): Clearly, the recorded y is not an observation on Y, but on the random variable Yw, which is said to be the weighted version of Y. This distribution a two-parameter is from the exponential family, it includes and generalizes the Poisson distribution by weighting. It is a discrete distribution that is more flexible than other weighted Poisson distributions that have been proposed for modeling underdispersed count data, for example, the extended Poisson distribution (Dimitrov and Kolev, 2000). We present some moment properties and we estimate its parameters. One classical example is considered to compare the fits of this new distribution with the extended Poisson distribution.

CONFIDENCE INTERVALS FOR THE EXPECTATION OF A POISSON VARIABLE

Biometrika ◽  
1959 ◽  
Vol 46 (3-4) ◽  
pp. 441-453 ◽  
Author(s):  
EDWIN L. CROW ◽  
ROBERT S. GARDNER
Keyword(s):  
Confidence Intervals ◽  
Poisson Variable

Confidence Limits for the Expectation of a Poisson Variable.

1961 ◽  
Vol 15 (74) ◽  
pp. 204
Author(s):  
J. Arthur Greenwood ◽  
Edwin L. Crow ◽  
Robert S. Gardner
Keyword(s):  
Confidence Limits ◽  
Poisson Variable
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