Relevant bibliographies by topics / Zero-truncated binomial distribution

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Academic literature on the topic 'Zero-truncated binomial distribution'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Zero-truncated binomial distribution"

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Sinha, Arun K., and Rajiv Kumar. "The Zero-Truncated Symmetrical Bivariate Negative Binomial Distribution." American Journal of Mathematical and Management Sciences 21, no. 1-2 (2001): 57–68. http://dx.doi.org/10.1080/01966324.2001.10737537.

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Mecha, Peter, Isaac Kipchirchir, George Muhua, and Joseph Ottieno. "Lifetime Distribution Based on Generators for Discrete Mixtures with Application to Lomax Distribution." American Journal of Theoretical and Applied Statistics 14, no. 2 (2025): 51–60. https://doi.org/10.11648/j.ajtas.20251402.11.

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In various application areas, data is frequently collected and analyzed using basic statistical distributions such as exponential, Poisson, and gamma distributions. However, these traditional distributions often fail to adequately capture the inherent heterogeneity present in real-world data. This limitation highlights the need for more flexible distributions that can address these complexities. Such distributions can be generated through techniques like reparameterization, generalization, compounding, and mixing. This paper focuses on deriving generators for survival functions of discrete mixtures using minimum and maximum order statistic distributions. The approach leverages the probability generating function (PGF) techniques of mixing distributions, including zero-truncated Poisson, shifted geometric, zero-truncated binomial, zero-truncated negative binomial, and logarithmic series distributions. Specifically, the derived generator was applied to Lomax distributions to construct survival functions. Consequently, the probability density function (PDF) and failure rate of the resulting discrete mixtures were also obtained. Furthermore, the paper examines the shapes of the PDF and failure rate for discrete mixtures derived from the zero-truncated Poisson distribution. Notably, the failure rates of discrete mixtures generated using minimum and maximum order statistics from the Lomax distribution exhibited distinct behaviors. The failure rate for the minimum order statistic was observed to decrease, while the failure rate for the maximum order statistic showed a combination of non-decreasing and bathtub-shaped patterns.
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Irshad, Muhammed Rasheed, Christophe Chesneau, Damodaran Santhamani Shibu, Mohanan Monisha, and Radhakumari Maya. "A Novel Generalization of Zero-Truncated Binomial Distribution by Lagrangian Approach with Applications for the COVID-19 Pandemic." Stats 5, no. 4 (2022): 1004–28. http://dx.doi.org/10.3390/stats5040060.

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The importance of Lagrangian distributions and their applicability in real-world events have been highlighted in several studies. In light of this, we create a new zero-truncated Lagrangian distribution. It is presented as a generalization of the zero-truncated binomial distribution (ZTBD) and hence named the Lagrangian zero-truncated binomial distribution (LZTBD). The moments, probability generating function, factorial moments, as well as skewness and kurtosis measures of the LZTBD are discussed. We also show that the new model’s finite mixture is identifiable. The unknown parameters of the LZTBD are estimated using the maximum likelihood method. A broad simulation study is executed as an evaluation of the well-established performance of the maximum likelihood estimates. The likelihood ratio test is used to assess the effectiveness of the third parameter in the new model. Six COVID-19 datasets are used to demonstrate the LZTBD’s applicability, and we conclude that the LZTBD is very competitive on the fitting objective.
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Jolayemi, E. Teju, and S. A. Aderoju. "On Zero-Truncated Negative Binomial with Excess Ones." Asian Journal of Probability and Statistics 22, no. 3 (2023): 45–50. http://dx.doi.org/10.9734/ajpas/2023/v22i3487.

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In this paper, Zero-truncated negative binomial distribution is modified to include excess ones to improve goodness-of-fit. This is necessary when data are dispersed and zero has been eliminated from data structurally. However, when the ones are unduly large, the proportion of this excess must be recognized and estimated to improve the fit. This development is applied using real data from a national survey.
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Ahmad, Zahoor, and Adil Rashid. "Maximum Entropy Formalism for Zero Truncated Poission and Binomial Distribution." Journal of Statistics Applications & Probability 6, no. 2 (2017): 441–44. http://dx.doi.org/10.18576/jsap/060218.

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Sitho, Surang, Sunthree Denthet, and Hira Nadeem. "Zero Truncated Negative Binomial Weighted Weibull Distribution and Its Application." Lobachevskii Journal of Mathematics 42, no. 13 (2021): 3241–52. http://dx.doi.org/10.1134/s1995080222010206.

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Bodhisuwan, Rujira, Sunthree Denthet, and Tannen Acoose. "Zero-Truncated Negative Binomial Weighted-Lindley Distribution and Its Application." Lobachevskii Journal of Mathematics 42, no. 13 (2021): 3105–11. http://dx.doi.org/10.1134/s1995080222010061.

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Tarami, Bahram, Mohsen Avaji, and Nahid Sanjari Farsipour. "Distributions Family of Extended Weibull Combined with Negative Binomial Distribution Truncated at Zero." Journal of Statistical Sciences 15, no. 1 (2021): 165–91. http://dx.doi.org/10.52547/jss.15.1.9.

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Neal, Allison T. "Distribution of clones among hosts for the lizard malaria parasite Plasmodium mexicanum." PeerJ 9 (November 2, 2021): e12448. http://dx.doi.org/10.7717/peerj.12448.

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Background Malaria parasites reproduce asexually, leading to the production of large numbers of genetically identical parasites, here termed a clonal line or clone. Infected hosts may harbor one or more clones, and the number of clones in a host is termed multiplicity of infection (MOI). Understanding the distribution of parasite clones among hosts can shed light on the processes shaping this distribution and is important for modeling MOI. Here, I determine whether the distribution of clones of the lizard malaria parasite Plasmodium mexicanum differ significantly from statistical distributions commonly used to model MOI and logical extensions of these models. Methods The number of clones per infection was assessed using four microsatellite loci with the maximum number of alleles at any one locus used as a simple estimate of MOI for each infection. I fit statistical models (Poisson, negative binomial, zero-inflated models) to data from four individual sites to determine a best fit model. I also simulated the number of alleles per locus using an unbiased estimate of MOI to determine whether the simple (but potentially biased) method I used to estimate MOI influenced model fit. Results The distribution of clones among hosts at individual sites differed significantly from traditional Poisson and negative binomial distributions, but not from zero-inflated modifications of these distributions. A consistent excess of two-clone infections and shortage of one-clone infections relative to all fit distributions was also observed. Any bias introduced by the simple method for estimating of MOI did not appear to qualitatively alter the results. Conclusions The statistical distributions used to model MOI are typically zero-truncated; truncating the Poisson or zero-inflated Poisson yield the same distribution, so the reasonable fit of the zero-inflated Poisson to the data suggests that the use of the zero-truncated Poisson in modeling is adequate. The improved fit of zero-inflated distributions relative to standard distributions may suggest that only a portion of the host population is located in areas suitable for transmission even at small sites (<1 ha). Collective transmission of clones and premunition may also contribute to deviations from standard distributions.
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Kyriakoussis, A., and Alex S. Papadopoulos. "The zero-truncated negative binomial distribution as a failure model from the Bayesian approach." Microelectronics Reliability 32, no. 1-2 (1992): 259–64. http://dx.doi.org/10.1016/0026-2714(92)90104-s.

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Book chapters on the topic "Zero-truncated binomial distribution"

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Flowerdew, Robin. "Modelling Migration with Poisson Regression." In Technologies for Migration and Commuting Analysis. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-755-8.ch014.

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Most statistical analysis is based on the assumption that error is normally distributed, but many data sets are based on discrete data (the number of migrants from one place to another must be a whole number). Recent developments in statistics have often involved generalising methods so that they can be properly applied to non-normal data. For example, Nelder and Wedderburn (1972) developed the theory of generalised linear modelling, where the dependent or response variable can take a variety of different probability distributions linked in one of several possible ways to a linear predictor, based on a combination of independent or explanatory variables. Several common statistical techniques are special cases of the generalised linear models, including the usual form of regression analysis, Ordinary Least Squares regression, and binomial logit modelling. Another important special case is Poisson regression, which has a Poisson-distributed dependent variable, linked logarithmically to a linear combination of independent variables. Poisson regression may be an appropriate method when the dependent variable is constrained to be a non-negative integer, usually a count of the number of events in certain categories. It assumes that each event is independent of the others, though the probability of an event may be linked to available explanatory variables. This chapter illustrates how Poisson regression can be carried out using the Stata package, proceeding to discuss various problems and issues which may arise in the use of the method. The number of migrants from area i to area j must be a non-negative integer and is likely to vary according to zone population, distance and economic variables. The availability of high-quality migration data through the WICID facility permits detailed analysis at levels from the region to the output areas. A vast range of possible explanatory variables can also be derived from the 2001 Census data. Model results are discussed in terms of the significant explanatory variables, the overall goodness of fit and the big residuals. Comparisons are drawn with other analytic techniques such as OLS regression. The relationship to Wilson’s entropy maximising methods is described, and variants on the method are explained. These include negative binomial regression and zero-censored and zero-truncated models.
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Conference papers on the topic "Zero-truncated binomial distribution"

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Bodhisuwan, Winai, Chookait Pudprommarat, Rujira Bodhisuwan, and Luckhana Saothayanun. "Zero-truncated negative binomial - Erlang distribution." In PROCEEDINGS OF THE 13TH IMT-GT INTERNATIONAL CONFERENCE ON MATHEMATICS, STATISTICS AND THEIR APPLICATIONS (ICMSA2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5012230.

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