The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Rajiv Kumar - One of the best experts on this subject based on the ideXlab platform.
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The Zero-Truncated Symmetrical Bivariate Negative Binomial Distribution
American Journal of Mathematical and Management Sciences, 2001Co-Authors: Arun Kumar Sinha, Rajiv KumarAbstract:SYNOPTIC ABSTRACTWhile dealing with many real life situations usually the frequency of the zeroth cell of a bivariate Distribution is either not available or difficult to record. Keeping this fact in view the symmetrical bivariate Negative Binomial Distribution has been modified in this paper, and the modified model has been referred to as the zero-truncated symmetrical bivariate Negative Binomial Distribution. Apart from fitting the model to an observed and reported bivariate data set, some new results are given which may help in understanding the chance mechanisms that generate the bivariate Distribution.
Blackdennis - One of the best experts on this subject based on the ideXlab platform.
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Quasi-Negative Binomial Distribution
Computational Statistics & Data Analysis, 2011Co-Authors: Lishubiao, Yangfang, Famoyefelix, Leecarl, BlackdennisAbstract:In this paper, a quasi-Negative Binomial Distribution (QNBD) derived from the class of generalized Lagrangian probability Distributions is studied. The Negative Binomial Distribution is a special c...
Naomichi Suzuki - One of the best experts on this subject based on the ideXlab platform.
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KNO scaling function of modified Negative Binomial Distribution
arXiv: High Energy Physics - Phenomenology, 1997Co-Authors: N. Nakajima, Minoru Biyajima, Naomichi SuzukiAbstract:We investigate the KNO scaling function of the modified Negative Binomial Distribution (MNBD), because this MNBD can explain the oscillating behaviors of the cumulant moment observed in $e^+e^-$ annihilations and in hadronic collisions. By using a straightforward method and the Poisson transform we derive the KNO scaling function from the MNBD. The KNO form of experimental data in $e^{+}e^{-}$ collisions and hadronic collisions are analyzed by the KNO scaling function of the MNBD and that of the Negative Binomial Distribution (NBD). The KNO scaling function of the MNBD describes the data as well as that of the NBD.
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Stochastic background of Negative Binomial Distribution
Physics Letters B, 1991Co-Authors: Naomichi Suzuki, Minoru Biyajima, G. WilkAbstract:Abstract A branching equation of the birth process with immigration is taken as a model for the particle production process. Using it we investigate cases in which its solution becomes the Negative Binomial Distribution. Furthermore, we compare our approach with the modified Negative Binomial Distribution proposed recently by Chliapnikov and Tchikilev and use it to analyse the observed multiplicity Distributions.
Dennis Black - One of the best experts on this subject based on the ideXlab platform.
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Quasi-Negative Binomial Distribution: Properties and applications
Computational Statistics & Data Analysis, 2011Co-Authors: Shubiao Li, Felix Famoye, Fang Yang, Dennis BlackAbstract:In this paper, a quasi-Negative Binomial Distribution (QNBD) derived from the class of generalized Lagrangian probability Distributions is studied. The Negative Binomial Distribution is a special case of QNBD. Some properties of QNBD, including the upper tail behavior and limiting Distributions, are investigated. It is shown that the moments do not exist in some situations and the limiting Distribution of QNBD is the generalized Poisson Distribution under certain conditions. A zero-inflated QNBD is also defined. Applications of QNBD and zero-inflated QNBD in various fields are presented and compared with some other existing Distributions including Poisson, generalized Poisson and Negative Binomial Distributions as well as their zero-inflated versions. In general, the QNBD or its zero-inflated version performs better than the other models based on the chi-square statistic and the Akaike Information Criterion, especially for the cases where the data are highly skewed, have heavy tails or excessive numbers of zeros.
Arun Kumar Sinha - One of the best experts on this subject based on the ideXlab platform.
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The Zero-Truncated Symmetrical Bivariate Negative Binomial Distribution
American Journal of Mathematical and Management Sciences, 2001Co-Authors: Arun Kumar Sinha, Rajiv KumarAbstract:SYNOPTIC ABSTRACTWhile dealing with many real life situations usually the frequency of the zeroth cell of a bivariate Distribution is either not available or difficult to record. Keeping this fact in view the symmetrical bivariate Negative Binomial Distribution has been modified in this paper, and the modified model has been referred to as the zero-truncated symmetrical bivariate Negative Binomial Distribution. Apart from fitting the model to an observed and reported bivariate data set, some new results are given which may help in understanding the chance mechanisms that generate the bivariate Distribution.
