Relevant bibliographies by topics / Poisson distribution

Academic literature on the topic 'Poisson distribution'

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Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Poisson distribution"

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Loukas, Sotirios, and H. Papageorgiou. "On a trivariate Poisson distribution." Applications of Mathematics 36, no. 6 (1991): 432–39. http://dx.doi.org/10.21136/am.1991.104480.

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SHANKER, Rama. "The Discrete Poisson-Aradhana Distribution." Turkiye Klinikleri Journal of Biostatistics 9, no. 1 (2017): 12–22. http://dx.doi.org/10.5336/biostatic.2017-54834.

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V. R., Saji Kumar. "α - Poisson Distribution." Calcutta Statistical Association Bulletin 54, no. 3-4 (September 2003): 275–80. http://dx.doi.org/10.1177/0008068320030312.

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Bidounga, R., P. C. Batsindila Nganga, L. Niéré, and D. Mizère. "A Note on the (Weighted) Bivariate Poisson Distribution." European Journal of Pure and Applied Mathematics 14, no. 1 (January 31, 2021): 192–203. http://dx.doi.org/10.29020/nybg.ejpam.v14i1.3895.

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In the recent statistical literature, the univariate Poisson distribution has been generalized by many authors, among them: the univariate weighted Poisson distribution [13], the generalized univariate Poisson distribution [7], the bivariate Poisson distribution according to Holgate [11], the bivariate Poisson distribution according to Lakshminarayana, Pandit and Srinivasa Rao [15], the bivariate Poisson distribution according to Berkhout and Plug [4], the bivariate weighted Poisson distribution according to Elion et al. [8] and the generalized bivariate Poisson distribution according to Famoye [9]. In this paper, We highlight the weighted bivariate Poisson distribution and show that it is the synthesis of all the bivariate Poisson distributions which, under certain conditions, converge in distribution towards the bivariate Poisson distribution according to Berkhout and Plug [4] which can be considered like the standard distribution in N2 as is the univariate Poisson distribution in N.
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Abd El-Monsef, Mohamed, and Nora Sohsah. "POISSON TRANSMUTED LINDLEY DISTRIBUTION." JOURNAL OF ADVANCES IN MATHEMATICS 11, no. 9 (January 1, 2016): 5631–38. http://dx.doi.org/10.24297/jam.v11i9.816.

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The main purpose of this paper is to introduce a new discrete compound distribution, namely Poisson Transmuted Lindley distribution (PTL) which offers a more flexible model for analyzing some types of countable data. The proposed distribution is accommodate unimodel, bathtub as well as decreasing failure rates. Most of the statistical and reliability measures are derived. For the estimation purposes the method of moment and maximum likelihood methods are studied for PTL. Simulation studies are conducted to investigate the performance of the maximum likelihood estimators. A real life application for PTL is introduced to test its goodness of fit and examine its performance compared with some other distributions.
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Deshmukh, S. R., and M. S. Kasture. "BIVARIATE DISTRIBUTION WITH TRUNCATED POISSON MARGINAL DISTRIBUTIONS." Communications in Statistics - Theory and Methods 31, no. 4 (May 14, 2002): 527–34. http://dx.doi.org/10.1081/sta-120003132.

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ARRATIA, RICHARD, A. D. BARBOUR, and SIMON TAVARÉ. "The Poisson–Dirichlet Distribution and the Scale-Invariant Poisson Process." Combinatorics, Probability and Computing 8, no. 5 (September 1999): 407–16. http://dx.doi.org/10.1017/s0963548399003910.

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We show that the Poisson–Dirichlet distribution is the distribution of points in a scale-invariant Poisson process, conditioned on the event that the sum T of the locations of the points in (0,1] is 1. This extends to a similar result, rescaling the locations by T, and conditioning on the event that T[les ]1. Restricting both processes to (0, β] for 0<β[les ]1, we give an explicit formula for the total variation distance between their distributions. Connections between various representations of the Poisson–Dirichlet process are discussed.
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Gao, Mingchu. "Compound bi-free Poisson distributions." Infinite Dimensional Analysis, Quantum Probability and Related Topics 22, no. 02 (June 2019): 1950014. http://dx.doi.org/10.1142/s0219025719500140.

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In this paper, we study compound bi-free Poisson distributions for two-faced families of random variables. We prove a Poisson limit theorem for compound bi-free Poisson distributions. Furthermore, a bi-free infinitely divisible distribution for a two-faced family of self-adjoint random variables can be realized as the limit of a sequence of compound bi-free Poisson distributions of two-faced families of self-adjoint random variables. If a compound bi-free Poisson distribution is determined by a positive number and the distribution of a two-faced family of finitely many random variables, which has an almost sure random matrix model, and the left random variables commute with the right random variables in the two-faced family, then we can construct a random bi-matrix model for the compound bi-free Poisson distribution. If a compound bi-free Poisson distribution is determined by a positive number and the distribution of a commutative pair of random variables, we can construct an asymptotic bi-matrix model with entries of creation and annihilation operators for the compound bi-free Poisson distribution.
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Rufin, Bidounda, Michel Koukouatikissa Diafouka, R. Ìeolie Foxie Miz Ìel Ìe Kitoti, and Dominique Miz`ere. "The Bivariate Extended Poisson Distribution of Type 1." European Journal of Pure and Applied Mathematics 14, no. 4 (November 10, 2021): 1517–29. http://dx.doi.org/10.29020/nybg.ejpam.v14i4.4151.

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In this paper, we will construct the bivariate extended Poisson distribution whichgeneralizes the univariate extended Poisson distribution. This law will be obtained by the method of the product of its marginal laws by a factor. This method was demonstrated in [7]. Thus we call the bivariate extended Poisson distribution of type 1 the bivariate extended Poisson distribution obtained by the method of the product of its marginal distributions by a factor. We will show that this distribution belongs to the family of bivariate Poisson distributions and and will highlight the conditions relating to the independence of the marginal variables. A simulation study was realised.
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Thavaneswaran, Aerambamoorthy, Saumen Mandal, and Dharini Pathmanathan. "Estimation for Wrapped Zero Inflated Poisson and Wrapped Poisson Distributions." International Journal of Statistics and Probability 5, no. 3 (April 8, 2016): 1. http://dx.doi.org/10.5539/ijsp.v5n3p1.

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There has been a growing interest in discrete circular models such as wrapped zero inflated Poisson and wrapped Poisson distributions and the trigonometric moments (see Brobbey et al., 2016 and Girija et al., 2014). Also, characteristic functions of stable processes have been used to study the estimation of the model parameters using estimating function approach (see Thavaneswaran et al., 2013). One difficulty in estimating the circular mean and the resultant mean length parameter of wrapped Poisson (WP) or wrapped zero inflated Poisson (WZIP) is that neither the likelihood of WP/WZIP random variable nor the score function is available in closed form, which leads one to use either trigonometric method of moment estimation (TMME) or an estimating function approach. In this paper, we study the estimation of WZIP distribution and WP distribution using estimating functions and obtain the closed form expression of the information matrix. We also derive the asymptotic distribution of the tangent of the mean direction for both the WZIP and WP distributions.
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Dissertations / Theses on the topic "Poisson distribution"

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Gu, Kangxia. "Testing the rates of Poisson distribution." Ann Arbor, Mich. : ProQuest, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3213456.

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Thesis (Ph.D. in Statistical Science)--S.M.U.
Title from PDF title page (viewed July 6, 2007). Source: Dissertation Abstracts International, Volume: 67-03, Section: B, page: 1504. Advisers: Hon Keung Tony Ng; William R. Schucany. Includes bibliographical references.
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Wang, Ling. "Homogeneity tests for several poisson populations." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/909.

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SILVA, PRISCILLA FERREIRA DA. "A BIVARIATE GARMA MODEL WITH CONDITIONAL POISSON DISTRIBUTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2013. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=22899@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Os modelos lineares generalizados auto regressivos com médias móveis (do inglês GARMA), possibilitam a modelagem de séries temporais de dados de contagem com estrutura de correlação similares aos dos modelos ARMA. Neste trabalho é desenvolvida uma extensão multivariada do modelo GARMA, considerando a especificação de um modelo Poisson bivariado a partir da distribuição de Kocherlakota e Kocherlakota (1992), a qual será denominada de modelo Poisson BGARMA. O modelo proposto é adequado para séries de contagens estacionárias, sendo possível, através de funções de ligação apropriadas, introduzir deterministicamente o efeito de sazonalidade e de tendência. A investigação das propriedades usuais dos estimadores de máxima verossimilhança (viés, eficiência e distribuição) foi realizada através de simulações de Monte Carlo. Com o objetivo de comparar o desempenho e a aderência do modelo proposto, este foi aplicado a dois pares de séries reais bivariadas de dados de contagem. O primeiro par de séries apresenta as contagens mensais de óbitos neonatais para duas faixas de dias de vida. O segundo par de séries refere-se a contagens de acidentes de automóveis diários em dois períodos: vespertino e noturno. Os resultados do modelo proposto, quando comparados com aqueles obtidos através do ajuste de um modelo Gaussiano bivariado Vector Autoregressive (VAR), indicam que o modelo Poisson BGARMA é capaz de capturar de forma adequada as variações de pares de séries de dados de contagem e de realizar previsões com erros aceitáveis, além de produzir previsões probabilísticas para as séries.
Generalized autoregressive linear models with moving average (GARMA) allow the modeling of discrete time series with correlation structure similar to those of ARMA’s models. In this work we developed an extension of a univariate Poisson GARMA model by considerating the specification of a bivariate Poisson model through the distribution presented on Kocherlakota and Kocherlakota (1992), which will be called Poisson BGARMA model. The proposed model not only is suitable for stationary discrete series, but also allows us to take into consideration the effect of seasonality and trend. The investigation of the usual properties of the maximum likelihood estimators (bias, efficiency and distribution) was performed using Monte Carlo simulations. Aiming to compare the performance and compliance of the proposed model, it was applied to two pairs of series of bivariate count data. The first pair is the monthly counts of neonatal deaths to two lanes of days. The second pair refers to counts of daily car accidents in two distinct periods: afternoon and evening. The results of our model when compared with those obtained by fitting a bivariate Vector Autoregressive Gaussian model (VAR) indicates that the Poisson BGARMA model is able to proper capture the variability of bivariate vectors of real time series of count data, producing forecasts with acceptable errors and allowing one to obtain probability forecasts.
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Wan, Wai-yin. "Analysis of Poisson count data using Geometric Process model." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37836493.

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Wan, Wai-yin, and 溫慧妍. "Analysis of Poisson count data using Geometric Process model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37836493.

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Buchmann, Boris. "Decompounding an estimation problem for the compound poisson distribution /." [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962736910.

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van, de Ven Remy Julius. "Estimation in mixed Poisson regression models." Thesis, The University of Sydney, 1996. https://hdl.handle.net/2123/26822.

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This thesis considers estimation of the parameters associated with models for count data displaying over-dispersion relative to the Poisson distribution where the over-dispersion is modelled using mixing. It is divided into seven chapters with Chapters Two to Five specific to the over-dispersed Poisson problem whilst Chapter Seven, which uses results from Chapter Six, is more general. The motivation for some of this work was the modelling of repeat counts of the number of fibres contained on microscopic slides as obtained by asbestos fibre counters and the subsequent estimation of mean fibre concentrations and counter variability. Chapter One introduces the above mentioned asbestos fibre problem and follows this with an overview of the thesis. In Chapter Two a model for repeated measures count data over-dispersed relative to the Poisson distribution appropriate to the asbestos problem is given. To accommodate the over-dispersion a Poisson random variable is compounded with a positive random variable with mean equal one and variance linked linearly, via a log function, to a set of covariates. Maximum likelihood estimators of the parameters are obtained for the case where the compounding distribution is gamma and extended quasi-likelihood parameter estimators are obtained when the compounding distribution is unspecified. These two sets of parameter estimators are then shown to be comparable in certain circumstances. In Chapter Three a special case of the general model in Chapter Two with a gamma compounding distribution is considered. Here repeat counts for a “subject” are taken as independent Poisson random variables with constant mean. The means are then modelled as independent observations from a gamma distribution. Two sets of moment estimators for the parameters of the model are obtained and generalized variances of the limiting distribution of the moment estimators are compared with the corresponding quantity for the maximum likelihood estimators. Also in this chapter we derive asymptotic results that explain some of the erratic behaviour of the moment estimators. Chapter Four considers the estimation of the shape parameter of the negative binomial distribution (NBD), this distribution being a special case of the model in Chapter Three. Here the results are given for a simulation study comparing four estimators for the shape parameter of the NBD distribution. Two criteria are used to compare the estimates obtained in the simulations, one being the traditional moment based criterion whilst the other is based on a new measure termed the “percentile measure”. This measure, based on the difference between the percentiles of the true and estimated distribution function, is argued to be more appropriate in many cases. In Chapter Five we continue studying the NBD and obtain some quantile related results. First we obtain bounds for the median in terms of the mean that are improvements on the bounds obtained by Payton, Young and Young (1989). Second we obtain percentile related bounds for the mean and use this to obtain a robust estimator for the mean of the NBD when the shape parameter is known. The remaining two chapters are devoted to robust estimation in (generalized) linear mixed models. In Chapter Six a modification to the Fellner (1991) procedure for robustly estimating variance components in normal linear mixed models is proposed and studied. Also given is a robust moment based method. These robust methods are then applied in Chapter Seven to the generalized linear mixed model to obtain robust parameter estimators and the behaviour of these new estimators is studied via a simulation study. From this simulation study in Chapter Seven it is concluded that the extension to generalized linear mixed models of the modification to Fellner’s method has merit. There should though be scope for improvement in the method and this could be a subject for further research. In particular, a possible mechanism for achieving an improvement would be to have more robust starting values for the variance components in the iterative procedure proposed. One solution would be to develop quantile based variance components estimates in the generalized linear mixed model and to use these as starting values. Another project for further research would be to obtain expressions for the variances of the fixed effects estimates for the linear mixed model obtained using the Fellner (1991) method. This would necessarily be an asymptotic result and of interest in its own right. However, once this was available the modified Fellner method of Chapter Six and its extension to generalized linear mixed models given in Chapter Seven could be improved. This is the case as the modification to Fellner’s method given in Chapter Six currently uses for these values the variances of the BLUP estimates of the fixed effects. Finally, an alternative robust estimation procedure based on the results of Windham (1995) could be another subject for further research. That paper, which assumes the data are independent and identically distributed (iid), proposes an estimation procedure that weights datum according to the value of the estimated likelihood at that datum point. The procedure seems to have merit based on the examples considered in Windham’s paper, which include some skewed distributions (e.g. gamma). Further research could consider extending the results to the non it'd case, in particular data from generalized linear mixed model problems. It may be possible using such a procedure in generalized linear mixed model cases to reduce some of the bias that is inherent in procedures based on Winsorization.
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Pfister, Mark. "Distribution of a Sum of Random Variables when the Sample Size is a Poisson Distribution." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3459.

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A probability distribution is a statistical function that describes the probability of possible outcomes in an experiment or occurrence. There are many different probability distributions that give the probability of an event happening, given some sample size n. An important question in statistics is to determine the distribution of the sum of independent random variables when the sample size n is fixed. For example, it is known that the sum of n independent Bernoulli random variables with success probability p is a Binomial distribution with parameters n and p: However, this is not true when the sample size is not fixed but a random variable. The goal of this thesis is to determine the distribution of the sum of independent random variables when the sample size is randomly distributed as a Poisson distribution. We will also discuss the mean and the variance of this unconditional distribution.
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Rodrigues, Cristiane. "Distribuições em série de potências modificadas inflacionadas e distribuição Weibull binominal negativa." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-28062011-095106/.

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Neste trabalho, alguns resultados, tais como, função geradora de momentos, relações de recorrência para os momentos e alguns teoremas da classe de distribuições em séries de potencias modificadas (MPSD) proposta por Gupta (1974) e da classe de distribuições em séries de potências modificadas inflacionadas (IMPSD) tanto em um ponto diferente de zero como no ponto zero são apresentados. Uma aplicação do Modelo Poisson padrão, do modelo binomial negativo padrão e dos modelos inflacionados de zeros para dados de contagem, ZIP e ZINB, utilizando-se as técnicas dos MLGs, foi realizada para dois conjuntos de dados reais juntamente com o gráfico normal de probabilidade com envelopes simulados. Também foi proposta a distribuição Weibull binomial negativa (WNB) que é bastante flexível em análise de dados positivos e foram estudadas algumas de suas propriedades matemáticas. Esta é uma importante alternativa para os modelos Weibull e Weibull geométrica, sub-modelos da WNB. A demostração de que a densidade da distribuição Weibull binomial negativa pode ser expressa como uma mistura de densidades Weibull é apresentada. Fornecem-se, também, seus momentos, função geradora de momentos, gráficos da assimetria e curtose, expressoes expl´citas para os desvios médios, curvas de Bonferroni e Lorenz, função quantílica, confiabilidade e entropia, a densidade da estat´stica de ordem e expressões explícita para os momentos da estatística de ordem. O método de máxima verossimilhança é usado para estimar os parametros do modelo. A matriz de informação esperada ´e derivada. A utilidade da distribuição WNB está ilustrada na an´alise de dois conjuntos de dados reais.
In this paper, some result such as moments generating function, recurrence relations for moments and some theorems of the class of modified power series distributions (MPSD) proposed by Gupta (1974) and of the class of inflated modified power series distributions (IMPSD) both at a different point of zero as the zero point are presented. The standard Poisson model, the standard negative binomial model and zero inflated models for count data, ZIP and ZINB, using the techniques of the GLMs, were used to analyse two real data sets together with the normal plot with simulated envelopes. The new distribution Weibull negative binomial (WNB) was proposed. Some mathematical properties of the WNB distribution which is quite flexible in analyzing positive data were studied. It is an important alternative model to the Weibull, and Weibull geometric distributions as they are sub-models of WNB. We demonstrate that the WNB density can be expressed as a mixture of Weibull densities. We provide their moments, moment generating function, plots of the skewness and kurtosis, explicit expressions for the mean deviations, Bonferroni and Lorenz curves, quantile function, reliability and entropy, the density of order statistics and explicit expressions for the moments of order statistics. The method of maximum likelihood is used for estimating the model parameters. The expected information matrix is derived. The usefulness of the new distribution is illustrated in two analysis of real data sets.
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Gagnon, Karine. "Distribution et abondance des larves d'éperlan arc-en-ciel (Osmerus mordax) au lac Saint-Jean /." Thèse, Chicoutimi : Université du Québec à Chicoutimi, 2005. http://theses.uqac.ca.

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Books on the topic "Poisson distribution"

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Barbour, A. D. Poisson approximation. Oxford [England]: Clarendon Press, 1992.

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Grandell, Jan. Mixed Poisson processes. London: Chapman & Hall, 1997.

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Lindsay, Glenn F. Recruiter productivity and the Poisson distribution. Monterey, Calif: Naval Postgraduate School, 1994.

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Generalized Poisson distributions: Properties and applications. New York: M. Dekker, 1989.

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Feng, Shui. The Poisson-Dirichlet Distribution and Related Topics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11194-5.

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The Poisson-Dirichlet distribution and related topics: Models and asymptotic behaviors. Heidelberg: Springer, 2010.

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Harris, Ian Richard. Smooth and predictive estimates for the compound Poisson distribution. Birmingham: University of Birmingham, 1987.

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Marijtje A. J. van Duijn. Mixed models for repeated count data. Leiden, Netherlands: DSWO Press, Leiden University, 1993.

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Heldt, John J. Quality sampling and reliability: New uses for the poisson distribution. Boca Raton: St. Lucie Press, 1999.

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A, Kutoyants Yu. Statistical inference for spatial Poisson processes. New York: Springer, 1998.

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Book chapters on the topic "Poisson distribution"

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Gooch, Jan W. "Poisson Distribution." In Encyclopedic Dictionary of Polymers, 991. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15324.

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Gooch, Jan W. "Poisson Distribution." In Encyclopedic Dictionary of Polymers, 546. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_8909.

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Weik, Martin H. "Poisson distribution." In Computer Science and Communications Dictionary, 1293. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_14247.

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Gooch, Jan W. "Poisson Ratio Distribution." In Encyclopedic Dictionary of Polymers, 546. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_8911.

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Nguyen, Hung T., and Gerald S. Rogers. "The Poisson Distribution." In Springer Texts in Statistics, 166–76. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-1013-9_20.

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Jolicoeur, Pierre. "The Poisson distribution." In Introduction to Biometry, 124–33. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4777-8_19.

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Cummings, Peter. "The Poisson Distribution." In Analysis of Incidence Rates, 53–82. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429055713-4.

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Russell, Kenneth G. "The Poisson Distribution." In Design of Experiments for Generalized Linear Models, 149–69. Boca Raton, Florida : CRC Press, [2019] | Series: Chapman & Hall/CRC interdisciplinary statistics: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429057489-5.

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Grandell, Jan. "The mixed Poisson distribution." In Mixed Poisson Processes, 13–50. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-3117-7_2.

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Feng, Shui. "The Poisson–Dirichlet Distribution." In Probability and its Applications, 15–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11194-5_2.

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Conference papers on the topic "Poisson distribution"

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Hubert, Paulo C., Marcelo S. Lauretto, Julio M. Stern, Paul M. Goggans, and Chun-Yong Chan. "FBST for Generalized Poisson Distribution." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2009. http://dx.doi.org/10.1063/1.3275617.

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SEETHA MAHALAXMI, D., and P. R. K. MURTI. "TAMPER RESISTANCE VIA POISSON DISTRIBUTION." In Proceedings of the 3rd Asian Applied Computing Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781860948534_0019.

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Adzkiah, A., D. Lestari, and L. Safitri. "Exponential Conway Maxwell Poisson distribution." In PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2020 (ISCPMS 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0059254.

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Fitria, Dina, Nonong Amalita, and Syafriandi. "Poisson Distribution with Discrete Parameter." In Proceedings of the 2nd International Conference on Mathematics and Mathematics Education 2018 (ICM2E 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/icm2e-18.2018.11.

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Özel, Gamze, and Selen Çakmakyapan. "A new generalized Poisson Lindley distribution." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4992404.

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Švihlík, Jan, Zuzana Krbcová, Jaromir Kukal, and Karel Fliegel. "Smoothing of astronomical images with Poisson distribution." In Applications of Digital Image Processing XL, edited by Andrew G. Tescher. SPIE, 2017. http://dx.doi.org/10.1117/12.2274121.

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Zamani, Hossein, Pouya Faroughi, and Noriszura Ismail. "Bivariate Poisson-weighted exponential distribution with applications." In PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4882600.

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Zhang, Hao Lan, Jiming Liu, Tongliang Li, Yun Xue, Songjie Xu, and Junhua Chen. "Extracting sample data based on poisson distribution." In 2017 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2017. http://dx.doi.org/10.1109/icmlc.2017.8108950.

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Yuanshu Jiang and Wenzhong Tang. "Poisson distribution-based page updating prediction strategy." In 2011 International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2011. http://dx.doi.org/10.1109/iccsnt.2011.6182119.

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Ramanujam, P. S., and N. Gronbech-Jensen. "Generation of sub-Poisson distribution of light." In Emerging OE Technologies, Bangalore, India, edited by Krishna Shenai, Ananth Selvarajan, C. K. N. Patel, C. N. R. Rao, B. S. Sonde, and Vijai K. Tripathi. SPIE, 1992. http://dx.doi.org/10.1117/12.636808.

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Reports on the topic "Poisson distribution"

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Lindsay, Glenn F. Recruiter Productivity and the Poisson Distribution. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada286230.

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Bryant, J. L., and A. S. Paulson. Estimation of the Parameters of a Modified Compound Poisson Distribution. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada178540.

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DeLacy, Brendan G., and Janon F. Embury. Infrared Extinction Coefficients of Aerosolized Conductive Flake Powders and Flake Suspensions having a Zero-Truncated Poisson Size Distribution. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada570956.

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Zacks, S., and Gang Li. The Distribution of the Size and Number of Shadows Cast on a Line Segment in a Poisson Random Field. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada233697.

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Vecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond, and Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45300.

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To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoelastic variables. This yields ensemble realizations of signal arrivals at specified locations where statistical properties of the signals can be estimated. Such ensemble predictions can be useful for preliminary site characterization, for military applications, and risk analysis for remote or inaccessible locations for which no data can be acquired. Comparison with experiments revealed that measured signals are not always within the predicted ranges of variability. Variance-based global sensitivity analysis has shown that the most significant parameters for signal amplitude predictions in the developed stochastic model are the uncertainty in the shear quality factor and the Poisson ratio above the water table depth.
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Guilfoyle, Michael, Ruth Beck, Bill Williams, Shannon Reinheimer, Lyle Burgoon, Samuel Jackson, Sherwin Beck, Burton Suedel, and Richard Fischer. Birds of the Craney Island Dredged Material Management Area, Portsmouth, Virginia, 2008-2020. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45604.

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This report presents the results of a long-term trend analyses of seasonal bird community data from a monitoring effort conducted on the Craney Island Dredged Material Management Area (CIDMMA) from 2008 to 2020, Portsmouth, VA. The USACE Richmond District collaborated with the College of William and Mary and the Coastal Virginia Wildlife Observatory, Waterbird Team, to conduct year-round semimonthly area counts of the CIDMMA to examine species presence and population changes overtime. This effort provides information on the importance of the area to numerous bird species and bird species’ groups and provides an index to those species and group showing significant changes in populations during the monitoring period. We identified those species regionally identified as Highest, High, and Moderate Priority Species based on their status as rare, sensitive, or in need of conservation attention as identified by the Atlantic Coast Joint Venture (ACJV), Bird Conservation Region (BCR), New England/Mid-Atlantic Bird Conservation Area (BCR 30). Of 134 ranked priority species in the region, the CIDMMA supported 102 of 134 (76%) recognized in the BCR, including 16 of 19 (84%) of Highest priority ranked species, 47 of 60 (78.3%) of High priority species, and 39 of 55 (71%) of Moderate priority species for BCR 30. All bird count and species richness data collected were fitted to a negative binomial (mean abundance) or Poisson distribution (mean species richness) and a total of 271 species and over 1.5 million birds were detected during the monitoring period. Most all bird species and species groups showed stable or increasing trends during the monitoring period. These results indicate that the CIDMMA is an important site that supports numerous avian species of local and regional conservation concern throughout the year.
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Tummala, Rohan, Andrew de Jesus, Natasha Tillett, Jeffrey Nelson, and Christine Lamey. Clinical and Socioeconomic Predictors of Palliative Care Utilization. University of Tennessee Health Science Center, January 2021. http://dx.doi.org/10.21007/com.lsp.2020.0006.

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INTRODUCTION: Palliative care continues to gain recognition among primary care providers, as patients suffering from chronic conditions may benefit from use of this growing service. OBJECTIVES: This single-institution quality improvement study investigates the clinical characteristics and socioeconomic status (SES) of palliative care patients and identifies predictors of palliative care utilization. METHODS: Retrospective chart review was used to compare clinical and SES parameters for three groups of patients: (1) palliative care patients who attended at least one visit since the inception of the University Clinical Health Palliative Care Clinic in Memphis, TN in October 2018 (n = 61), (2) palliative care patients who did not attend any appointments (n = 19), and (3) a randomized group of age-matched primary care patients seen by one provider from May 2018 to May 2019 (n = 36). A Poisson regression model with backward conditional variable selection was used to determine predictors of palliative care utilization. RESULTS: Patients across the three care groups did not differ in demographic parameters. Compared to palliative care-referred non-users and primary care patients, palliative care patients tended to have lower health risk (p < 0.001). Palliative care patients did not differ from primary care patients in socioeconomic status but did differ in comorbidity distribution, having a higher prevalence of cancer (𝜒2 = 14.648, df = 7, p = 0.041). Chance of 10-year survival did not differ across risk categories for palliative care patients but was significantly lower for very high-risk compared to moderate-risk primary care patients (30% vs. 78%, p = 0.019). Significant predictors of palliative care use and their corresponding incidence rate ratios (IRR) were hospital referral (IRR = 1.471; p = 0.039), higher number of prescribed medications (IRR = 1.045; p = 0.003), lower Charlson Comorbidity Index (IRR = 0.907; p = 0.003), and lower systolic blood pressure (IRR = 0.989; p = 0.004). CONCLUSIONS: Patients who are expected to benefit from and of being high utilizers of palliative care may experience greater clinical benefit from earlier referral to this service.
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