Journal articles: 'Zero-inflated generalized Poisson model'

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Angers, Jean-François, and Atanu Biswas. "A Bayesian analysis of zero-inflated generalized Poisson model." Computational Statistics & Data Analysis 42, no. 1-2 (February 2003): 37–46. http://dx.doi.org/10.1016/s0167-9473(02)00154-8.

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Gupta, Pushpa Lata, Ramesh C. Gupta, and Ram C. Tripathi. "Score Test for Zero Inflated Generalized Poisson Regression Model." Communications in Statistics - Theory and Methods 33, no. 1 (January 4, 2005): 47–64. http://dx.doi.org/10.1081/sta-120026576.

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Zhao, Weihua, Riquan Zhang, Jicai Liu, and Yazhao Lv. "Semi Varying Coefficient Zero-Inflated Generalized Poisson Regression Model." Communications in Statistics - Theory and Methods 44, no. 1 (December 3, 2014): 171–85. http://dx.doi.org/10.1080/03610926.2012.735325.

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Zuo, Guoxin, Kang Fu, Xianhua Dai, and Liwei Zhang. "Generalized Poisson Hurdle Model for Count Data and Its Application in Ear Disease." Entropy 23, no. 9 (September 13, 2021): 1206. http://dx.doi.org/10.3390/e23091206.

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For count data, though a zero-inflated model can work perfectly well with an excess of zeroes and the generalized Poisson model can tackle over- or under-dispersion, most models cannot simultaneously deal with both zero-inflated or zero-deflated data and over- or under-dispersion. Ear diseases are important in healthcare, and falls into this kind of count data. This paper introduces a generalized Poisson Hurdle model that work with count data of both too many/few zeroes and a sample variance not equal to the mean. To estimate parameters, we use the generalized method of moments. In addition, the asymptotic normality and efficiency of these estimators are established. Moreover, this model is applied to ear disease using data gained from the New South Wales Health Research Council in 1990. This model performs better than both the generalized Poisson model and the Hurdle model.

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Faroughi, Pouya, and Noriszura Ismail. "Bivariate zero-inflated generalized Poisson regression model with flexible covariance." Communications in Statistics - Theory and Methods 46, no. 15 (April 21, 2017): 7769–85. http://dx.doi.org/10.1080/03610926.2016.1165846.

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Zamani, Hossein, and Noriszura Ismail. "Functional Form for the Zero-Inflated Generalized Poisson Regression Model." Communications in Statistics - Theory and Methods 43, no. 3 (January 8, 2014): 515–29. http://dx.doi.org/10.1080/03610926.2012.665553.

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SULISTYANINGSIH, NI WAYAN AMANDA DEWI, I. KOMANG GDE SUKARSA, and NI LUH PUTU SUCIPTAWATI. "PENERAPAN REGRESI ZERO INFLATED GENERALIZED POISSON (ZIGP) PADA DATA OVERDISPERSION." E-Jurnal Matematika 8, no. 1 (February 2, 2019): 1. http://dx.doi.org/10.24843/mtk.2019.v08.i01.p228.

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Zero Inflated Generalized Poisson (ZIGP) is a regression model used to analyze Poisson distributed discrete data which contains mostly zero and tends to experience overdispersion (varians value greater than the mean value). The purpose of this research is to find out the best model and the factors which influence the maternal mortality in Bali Province in year 2016 by using ZIGP regression model. The data used in this research was data from health profile Bali Province with the object totally 57 district rate data has proportion of zeros value more than 50% on the response variable. The analysis result of ZIGP data on maternal mortality cannot modeled using the ZIGP so ZIGP regression model became ZIP model . The best model which resulted from ZIP regression got one free variable which have significant impact towards the total number of maternal mortality. This significant variabel is the percentage of mother did visiting to K1.

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Ye, Peng, Wan Tang, Jiang He, and Hua He. "A GEE-type approach to untangle structural and random zeros in predictors." Statistical Methods in Medical Research 28, no. 12 (November 26, 2018): 3683–96. http://dx.doi.org/10.1177/0962280218812228.

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Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration.

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Li, Qiuya, Geoffrey KF Tso, Yichen Qin, Travis I. Lovejoy, Timothy G. Heckman, and Yang Li. "Penalized multiple inflated values selection method with application to SAFER data." Statistical Methods in Medical Research 28, no. 10-11 (September 19, 2018): 3205–25. http://dx.doi.org/10.1177/0962280218797148.

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Expanding on the zero-inflated Poisson model, the multiple-inflated Poisson model is applied to analyze count data with multiple inflated values. The existing studies on the multiple-inflated Poisson model determined the inflated values by inspecting the histogram of count response and fitting the model with different combinations of inflated values, which leads to relatively complicated computations and may overlook some real inflated points. We address a two-stage inflated values selection method, which takes all values of count response as potential inflated values and adopts the adaptive lasso regularization on the mixing proportion of those values. Numerical studies demonstrate the excellent performance both on inflated values selection and parameters estimation. Moreover, a specially designed simulation, based on the structure of data from a randomized clinical trial of an HIV sexual risk education intervention, performs well and ensures our method could be generalized to the real situation. An empirical analysis of a clinical trial dataset is used to elucidate the multiple-inflated Poisson model.

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Lee, Sangyeol, Youngmi Lee, and Cathy W. S. Chen. "Parameter change test for zero-inflated generalized Poisson autoregressive models." Statistics 50, no. 3 (October 5, 2015): 540–57. http://dx.doi.org/10.1080/02331888.2015.1083020.

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Xie, Feng-Chang, Bo-Cheng Wei, and Jin-Guan Lin. "Score tests for zero-inflated generalized Poisson mixed regression models." Computational Statistics & Data Analysis 53, no. 9 (July 2009): 3478–89. http://dx.doi.org/10.1016/j.csda.2009.02.017.

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Xie, Feng-Chang, Jin-Guan Lin, and Bo-Cheng Wei. "Bayesian zero-inflated generalized Poisson regression model: estimation and case influence diagnostics." Journal of Applied Statistics 41, no. 6 (December 20, 2013): 1383–92. http://dx.doi.org/10.1080/02664763.2013.871508.

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Purhadi, Dewi Novita Sari, Qurotul Aini, and Irhamah. "Geographically weighted bivariate zero inflated generalized Poisson regression model and its application." Heliyon 7, no. 7 (July 2021): e07491. http://dx.doi.org/10.1016/j.heliyon.2021.e07491.

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Neelon, Brian, Howard H. Chang, Qiang Ling, and Nicole S. Hastings. "Spatiotemporal hurdle models for zero-inflated count data: Exploring trends in emergency department visits." Statistical Methods in Medical Research 25, no. 6 (September 30, 2016): 2558–76. http://dx.doi.org/10.1177/0962280214527079.

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Motivated by a study exploring spatiotemporal trends in emergency department use, we develop a class of two-part hurdle models for the analysis of zero-inflated areal count data. The models consist of two components—one for the probability of any emergency department use and one for the number of emergency department visits given use. Through a hierarchical structure, the models incorporate both patient- and region-level predictors, as well as spatially and temporally correlated random effects for each model component. The random effects are assigned multivariate conditionally autoregressive priors, which induce dependence between the components and provide spatial and temporal smoothing across adjacent spatial units and time periods, resulting in improved inferences. To accommodate potential overdispersion, we consider a range of parametric specifications for the positive counts, including truncated negative binomial and generalized Poisson distributions. We adopt a Bayesian inferential approach, and posterior computation is handled conveniently within standard Bayesian software. Our results indicate that the negative binomial and generalized Poisson hurdle models vastly outperform the Poisson hurdle model, demonstrating that overdispersed hurdle models provide a useful approach to analyzing zero-inflated spatiotemporal data.

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Chen, Xue-Dong, Ying-Zi Fu, and Xue-Ren Wang. "Local influence measure of zero-inflated generalized Poisson mixture regression models." Statistics in Medicine 32, no. 8 (August 17, 2012): 1294–312. http://dx.doi.org/10.1002/sim.5560.

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Famoye, Felix, and Karan P. Singh. "Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data." Journal of Data Science 4, no. 1 (July 13, 2021): 117–30. http://dx.doi.org/10.6339/jds.2006.04(1).257.

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Tawiah, Kassim, Samuel Iddi, and Anani Lotsi. "On Zero-Inflated Hierarchical Poisson Models with Application to Maternal Mortality Data." International Journal of Mathematics and Mathematical Sciences 2020 (December 28, 2020): 1–8. http://dx.doi.org/10.1155/2020/1407320.

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Count outcomes are commonly encountered in health sector data. The occurrence of count outcomes that exhibit many zeros has necessitated the extension of the ubiquitous Poisson regression model to accommodate the zero inflation and overdispersion as a result of the extra dispersion. We explored different extensions of the Poisson model including mixed models within the generalized linear mixed model framework to account for the repeated measurement of outcomes. These models are applied to maternal mortality data from fifty-six health facilities in four regions of Ghana. The objective is to identify factors associated with maternal mortality. The best-fitting model, the zero-inflated Poisson generalized linear mixed model, revealed that maternal mortality in hospital facilities is influenced by the number of referrals (into and out) of the hospital facility, number of antenatal visits exceeding four, number of midwives, and number of medical doctors at the facility. To be able to achieve targeted results in reducing maternal mortality and achieve the Sustainable Development Goal 3, the government, together with the ministry of health, should provide adequate maternal health services, especially at the district and community level. Additionally, there is a need for increased investment in Community Health Planning Services and related healthcare infrastructure and systems within the context of the Ouagadougou Declaration, that is, improve the training of skilled birth workers (midwives and doctors) and employ them at clinics to deal with labour complications without referring them to major hospitals. Furthermore, a well-structured awareness campaign is needed with importance given to avoiding adolescent pregnancy and improving antenatal care attendance to, at least, four, the gold standard, before delivery. Also, we recommend quality assessment form an essential part of all services that are directed towards improving maternal health and that more emphasis is needed to be given on research with multiple allied partners.

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Affleck, David LR. "Poisson mixture models for regression analysisof stand-level mortality." Canadian Journal of Forest Research 36, no. 11 (November 1, 2006): 2994–3006. http://dx.doi.org/10.1139/x06-189.

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Periodic stand-level mortality data from permanent plots tend to be highly variable, skewed, and frequently contain many zero observations. Such data have commonly been modeled using nonlinear mortality functions fitted by least squares, and more recently by a two stage approach incorporating a logistic regression step. This study describes a set of nonlinear regression models that structure stochastic variation about a mortality function according to basic probability distributions appropriate for non-negative count data, including the Poisson, negative binomial (NB), and generalized Poisson (GP). Also considered are zero-inflated and hurdle modifications of these basic models. The models are developed and fit to mortality data from a loblolly pine (Pinus taeda L.) spacing trial with a conspicuous mode at 0. The sample data exhibit more variability than can be accommodated by a Poisson or modified Poisson model; the NB and GP models incorporate the extra-Poisson dispersion and offer an improved fit. A hurdle NB model best describes this sample, but, like the zero-inflated models and two-stage approach, modifies the interpretation of the mean structure and raises the question of overfitting. Considering both data-model agreement and the biological relevance of these models' components, the analysis suggests that the NB model offers a more compelling and credible inferential basis for fitting stand-level mortality functions.

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Xu, Xiaofei, Ying Chen, Cathy W. S. Chen, and Xiancheng Lin. "Adaptive log-linear zero-inflated generalized Poisson autoregressive model with applications to crime counts." Annals of Applied Statistics 14, no. 3 (September 2020): 1493–515. http://dx.doi.org/10.1214/20-aoas1360.

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Xie, Feng‐Chang, Bo‐Cheng Wei, and Jin‐Guan Lin. "Assessing influence for pharmaceutical data in zero‐inflated generalized Poisson mixed models." Statistics in Medicine 27, no. 18 (August 15, 2008): 3656–73. http://dx.doi.org/10.1002/sim.3233.

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Lawal, Bayo H. "GLM for Some Class of Com-Poisson Distributions with Applications." International Journal of Statistics and Probability 7, no. 6 (August 17, 2018): 1. http://dx.doi.org/10.5539/ijsp.v7n6p1.

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In this paper, we present regression models (GLM) for the class of Conway-Maxwell-Poisson (Com-Poisson) distributions. This class of models include the Com-Poisson, the Com-Poisson negative binomial, the Generalized Com-Poisson and the Extended Com-Poisson distributions, all of which have been presented in various literatures within the last five years. While these distributions have been applied most especially to frequency count data exhibiting over or under dispersion, not much has been presented in the application of this class of models to data having several covariates (the exception being the Com-Poisson itself). Thus in this paper, we present the generalized linear model formulation for these distributions and compare our results with the baseline Com-Poisson and Poisson models. Two data sets are employed in this application. We further extended our discussion to the zero-inflated versions of these distributions and applying same to a well established data with having 64\% zero observations. All the models are fitted using SAS PROC NLMIXED. In all cases, empirical means and variances are generated which leads to our ability to compute the Wald's goodness-of-fit test statistic for all the models employed in this paper.

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Sarvi, Fatemeh, Abbas Moghimbeigi, and Hossein Mahjub. "GEE-based zero-inflated generalized Poisson model for clustered over or under-dispersed count data." Journal of Statistical Computation and Simulation 89, no. 14 (June 25, 2019): 2711–32. http://dx.doi.org/10.1080/00949655.2019.1632857.

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Chi, Jinling, Ying Zhou, Lili Chen, and Yajing Zhou. "Bayesian interval mapping of count trait loci based on zero‐inflated generalized Poisson regression model." Biometrical Journal 62, no. 6 (May 12, 2020): 1428–42. http://dx.doi.org/10.1002/bimj.201900274.

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Bonat, Wagner H., Bent Jørgensen, Célestin C. Kokonendji, John Hinde, and Clarice G. B. Demétrio. "Extended Poisson–Tweedie: Properties and regression models for count data." Statistical Modelling 18, no. 1 (August 30, 2017): 24–49. http://dx.doi.org/10.1177/1471082x17715718.

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We propose a new class of discrete generalized linear models based on the class of Poisson–Tweedie factorial dispersion models with variance of the form [Formula: see text], where [Formula: see text] is the mean and [Formula: see text] and [Formula: see text] are the dispersion and Tweedie power parameters, respectively. The models are fitted by using an estimating function approach obtained by combining the quasi-score and Pearson estimating functions for the estimation of the regression and dispersion parameters, respectively. This provides a flexible and efficient regression methodology for a comprehensive family of count models including Hermite, Neyman Type A, Pólya–Aeppli, negative binomial and Poisson-inverse Gaussian. The estimating function approach allows us to extend the Poisson–Tweedie distributions to deal with underdispersed count data by allowing negative values for the dispersion parameter [Formula: see text]. Furthermore, the Poisson–Tweedie family can automatically adapt to highly skewed count data with excessive zeros, without the need to introduce zero-inflated or hurdle components, by the simple estimation of the power parameter. Thus, the proposed models offer a unified framework to deal with under-, equi-, overdispersed, zero-inflated and heavy-tailed count data. The computational implementation of the proposed models is fast, relying only on a simple Newton scoring algorithm. Simulation studies showed that the estimating function approach provides unbiased and consistent estimators for both regression and dispersion parameters. We highlight the ability of the Poisson–Tweedie distributions to deal with count data through a consideration of dispersion, zero-inflated and heavy tail indices, and illustrate its application with four data analyses. We provide an R implementation and the datasets as supplementary materials.

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Mahmoodi, Marzieh, Abbas Moghimbeigi, Kazem Mohammad, and Javad Faradmal. "Semiparametric models for multilevel overdispersed count data with extra zeros." Statistical Methods in Medical Research 27, no. 4 (July 7, 2016): 1187–201. http://dx.doi.org/10.1177/0962280216657376.

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This study proposes semiparametric models for analysis of hierarchical count data containing excess zeros and overdispersion simultaneously. The methods discussed in this paper handle nonlinear covariate effects through flexible semiparametric multilevel regression techniques. This is performed by providing a comprehensive comparison of semiparametric multilevel zero-inflated negative binomial and semiparametric multilevel zero-inflated generalized Poisson models under the real and simulated data. An EM algorithm based on Newton–Raphson equations for maximum penalized likelihood estimation approach is developed. The performance of the proposed models is assessed by using a Monte Carlo simulation study. We also illustrated the methods by the analysis of decayed, missing, and filled teeth of children aged 5–14 years old.

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Hilliard, J., D. Karlen, T. Dix, S. Markham, and A. Schulze. "Comparative species abundance modeling of Capitellidae (Annelida) in Tampa Bay, Florida, USA." Marine Ecology Progress Series 653 (October 29, 2020): 105–19. http://dx.doi.org/10.3354/meps13484.

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Capitellid polychaetes are ubiquitous throughout the world’s oceans and are often encountered in high abundance. We used an extensive dataset of species abundance and distribution records of the Capitella capitata complex, C. aciculata, C. jonesi, Heteromastus filiformis, Mediomastus ambiseta, and M. californiensis from Tampa Bay, Florida, USA, as a model system of closely related species filling a similar ecological niche. We sought to (1) characterize the spatial distribution of each species, (2) determine if a single species abundance modeling strategy could be applied to them all, and (3) assess environmental drivers of species distribution and abundance. We found that all species had a zero-inflated abundance distribution and there was spatial autocorrelation by bay regions. Lorenz curves were an effective tool to assess spatial patterns of species abundance across large areas. Bay segment, depth, and dissolved oxygen were the most important environmental drivers. Modeling was accomplished by comparing 6 different approaches: 4 generalized additive models (GAMs: Poisson, negative binomial, Tweedie, and zero-inflated Poisson distributions), hurdle models, and boosted regression trees. There was no single model with top performance for every species. However, GAM-Tweedie and hurdle models performed well overall and may be useful for studies of other benthic marine invertebrates.

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Calderín-Ojeda, Enrique, Emilio GóMez-Déniz, and Inmaculada Barranco-Chamorro. "MODELLING ZERO-INFLATED COUNT DATA WITH A SPECIAL CASE OF THE GENERALISED POISSON DISTRIBUTION." ASTIN Bulletin 49, no. 03 (September 2019): 689–707. http://dx.doi.org/10.1017/asb.2019.26.

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AbstractA one-parameter version of the generalised Poisson distribution provided by Consul and Jain (1973) is considered in this paper. The distribution is unimodal with a zero vertex and over-dispersed. A generalised linear model related to this distribution is also presented. Its parameters can be estimated by using a Fisher-Scoring algorithm which is equivalent to iteratively reweighted least squares. Due to its flexibility and capacity to describe highly skewed data with an excessive number of zeros, the model is suitable to be applied in insurance settings as an alternative to the negative binomial and zero-inflated model.

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Cui, Yuehua, and Wenzhao Yang. "Zero-inflated generalized Poisson regression mixture model for mapping quantitative trait loci underlying count trait with many zeros." Journal of Theoretical Biology 256, no. 2 (January 2009): 276–85. http://dx.doi.org/10.1016/j.jtbi.2008.10.003.

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Farhadi Hassankiadeh, Roghaye, Anoshirvan Kazemnejad, Mohammad Gholami Fesharaki, and Siamak Kargar Jahromi. "Efficiency of Zero-Inflated Generalized Poisson Regression Model on Hospital Length of Stay Using Real Data and Simulation Study." Caspian Journal of Health Research 3, no. 1 (March 1, 2018): 5–9. http://dx.doi.org/10.29252/cjhr.3.1.5.

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Czado, Claudia, Vinzenz Erhardt, Aleksey Min, and Stefan Wagner. "Zero-inflated generalized Poisson models with regression effects on the mean, dispersion and zero-inflation level applied to patent outsourcing rates." Statistical Modelling: An International Journal 7, no. 2 (July 2007): 125–53. http://dx.doi.org/10.1177/1471082x0700700202.

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Moineddin, Rahim, Christopher Meaney, and Eva Grunfeld. "On the analysis of composite measures of quality in medical research." Statistical Methods in Medical Research 26, no. 2 (October 8, 2014): 633–60. http://dx.doi.org/10.1177/0962280214553330.

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Composite endpoints are commonplace in biomedical research. The complex nature of many health conditions and medical interventions demand that composite endpoints be employed. Different approaches exist for the analysis of composite endpoints. A Monte Carlo simulation study was employed to assess the statistical properties of various regression methods for analyzing binary composite endpoints. We also applied these methods to data from the BETTER trial which employed a binary composite endpoint. We demonstrated that type 1 error rates are poor for the Negative Binomial regression model and the logistic generalized linear mixed model (GLMM). Bias was minimal and power was highest in the binomial logistic regression model, the linear regression model, the Poisson (corrected for over-dispersion) regression model and the common effect logistic generalized estimating equation (GEE) model. Convergence was poor in the distinct effect GEE models, the logistic GLMM and some of the zero-one inflated beta regression models. Considering the BETTER trial data, the distinct effect GEE model struggled with convergence and the collapsed composite method estimated an effect, which was greatly attenuated compared to other models. All remaining models suggested an intervention effect of similar magnitude. In our simulation study, the binomial logistic regression model (corrected for possible over/under-dispersion), the linear regression model, the Poisson regression model (corrected for over-dispersion) and the common effect logistic GEE model appeared to be unbiased, with good type 1 error rates, power and convergence properties.

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Raquel, Gabriela C., Katiane S. Conceição, Marcos O. Prates, and Marinho G. Andrade. "A zero-modified Poisson mixed model with generalized random effect." Journal of Statistical Computation and Simulation 91, no. 12 (March 7, 2021): 2457–74. http://dx.doi.org/10.1080/00949655.2021.1898612.

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MacNeil, M. Aaron, John K. Carlson, and Lawrence R. Beerkircher. "Shark depredation rates in pelagic longline fisheries: a case study from the Northwest Atlantic." ICES Journal of Marine Science 66, no. 4 (February 25, 2009): 708–19. http://dx.doi.org/10.1093/icesjms/fsp022.

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Abstract MacNeil, M. A., Carlson, J. K., and Beerkircher, L. R. 2009. Shark depredation rates in pelagic longline fisheries: a case study from the Northwest Atlantic. – ICES Journal of Marine Science, 66: 708–719. A suite of modelling approaches was employed to analyse shark depredation rates from the US Atlantic pelagic longline fishery. As depredation events are relatively rare, there are a large number of zeroes in pelagic longline data and conventional generalized linear models (GLMs) may be ineffective as tools for statistical inference. GLMs (Poisson and negative binomial), two-part (delta-lognormal and truncated negative binomial, T-NB), and mixture models (zero-inflated Poisson, ZIP, and zero-inflated negative binomial, ZINB) were used to understand the factors that contributed most to the occurrence of depredation events that included a small proportion of whale damage. Of the six distribution forms used, only the ZIP and T-NB models performed adequately in describing depredation data, and the T-NB and ZINB models outperformed the ZIP models in bootstrap cross-validation estimates of prediction error. Candidate T-NB and ZINB model results showed that encounter probabilities were more strongly related to large-scale covariates (space, season) and that depredation counts were correlated with small-scale characteristics of the fishery (temperature, catch composition). Moreover, there was little evidence of historical trends in depredation rates. The results show that the factors contributing to most depredation events are those already controlled by ships' captains and, beyond novel technologies to repel sharks, there may be little more to do to reduce depredation loss in the fishery within current economic and operational constraints.

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Mielenz, N., K. Thamm, M. Bulang, and J. Spilke. "Generalized linear models with random effects for the description of data with excess zeros." Archives Animal Breeding 54, no. 6 (October 10, 2011): 661–75. http://dx.doi.org/10.5194/aab-54-661-2011.

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Abstract. In this paper count data with excess zeros and repeated observations per subject are evaluated. If the number of values observed for the zero event in the trial substantially exceeds the expected number (derived from the Poisson or from the negative binomial distribution), then there is an excess of zeros. Hurdle and zero-inflated models with random effects are available in order to evaluate this type of data. In this paper both model approaches are presented and are used for the evaluation of the number of visits to the feeder per cow per hour. Finally, for the analysis of the target trait a hurdle model with random effects based on a negative binomial distribution was used. This analysis was derived from a detailed comparison of models and was needed because of a simpler computer implementation. For improved interpretation of the results, the levels of the explanatory factors (for example, the classes of lactation) were not averaged in the link scale, but rather in the response scale. The deciding explanatory variables for the pattern of visiting activities in the 24-hour cycle are the milking and cleaning times at hours 4, 7, 12 and 20. The highly significant differences in the visiting frequencies of cows of the first lactation and those of higher lactations were explained by competition for access to the feeder and thus to the feed.

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Bhati, Deepesh, DVS Sastry, and PZ Maha Qadri. "A New Generalized Poisson-Lindley Distribution: Applications and Properties." Austrian Journal of Statistics 44, no. 4 (December 6, 2015): 35–51. http://dx.doi.org/10.17713/ajs.v44i4.54.

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A new generalized Poisson Lindley distribution is obtained by compounding Poissondistribution with two parameter generalised Lindley distribution. The new distribution isshown to be unimodal and over dispersed. This distribution has a tendency to accommodate right tail as well as for particular values of parameter the tail tends to zero at a faster rate. Various properties like cumulative distribution function, generating function, Moments etc. are derived. Knowledge about the parameters is obtained through Method of Moments, Maximum Likelihood Method and EM Algorithm. Moreover, an actuarial application in collective risk model is shown by considering the proposed distribution as primary and Exponential and Erlang as secondary distribution. The model is applied to real dataset and found to perform better than competing models.

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GAVISH, N., and K. PROMISLOW. "On the structure of generalized Poisson–Boltzmann equations." European Journal of Applied Mathematics 27, no. 4 (November 20, 2015): 667–85. http://dx.doi.org/10.1017/s0956792515000613.

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In this work, we analyse a broad class of generalized Poisson–Boltzmann equations and reveal a common mathematical structure. In the limit of a wide electrode, we show that a broad class of generalized Poisson–Boltzmann equations admits a reduction that affords an explicit connection between the functional form of the corresponding free energy and the associated differential capacitance data. We exploit the relation to we show that differential capacitance curves generically undergo an inflection transition with increasing salt concentration, shifting from a local minimum near the point of zero charge for dilute solutions to a local maximum point near the point of zero charge for concentrated solutions. In addition, we develop a robust numerical method for solving generalized Poisson–Boltzmann equations which is easily applicable to the broad class of generalized Poisson–Boltzmann equations with very few code adjustments required for each model

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Fan, Liqiong, Sharon D. Yeatts, Bethany J. Wolf, Leslie A. McClure, Magdy Selim, and Yuko Y. Palesch. "The impact of covariate misclassification using generalized linear regression under covariate–adaptive randomization." Statistical Methods in Medical Research 27, no. 1 (November 23, 2015): 20–34. http://dx.doi.org/10.1177/0962280215616405.

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Under covariate adaptive randomization, the covariate is tied to both randomization and analysis. Misclassification of such covariate will impact the intended treatment assignment; further, it is unclear what the appropriate analysis strategy should be. We explore the impact of such misclassification on the trial’s statistical operating characteristics. Simulation scenarios were created based on the misclassification rate and the covariate effect on the outcome. Models including unadjusted, adjusted for the misclassified, or adjusted for the corrected covariate were compared using logistic regression for a binary outcome and Poisson regression for a count outcome. For the binary outcome using logistic regression, type I error can be maintained in the adjusted model, but the test is conservative using an unadjusted model. Power decreased with both increasing covariate effect on the outcome as well as the misclassification rate. Treatment effect estimates were biased towards the null for both the misclassified and unadjusted models. For the count outcome using a Poisson model, covariate misclassification led to inflated type I error probabilities and reduced power in the misclassified and the unadjusted model. The impact of covariate misclassification under covariate–adaptive randomization differs depending on the underlying distribution of the outcome.

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Aini, Q., Purhadi, and Irhamah. "Bivariate zero inflated generalized Poisson regression model in the number of pregnant maternal mortality and the number of postpartum maternal mortality in the Central Java Province in 2017." Journal of Physics: Conference Series 1511 (March 2020): 012055. http://dx.doi.org/10.1088/1742-6596/1511/1/012055.

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Bidounga, Rufin, Evrand Giles Brunel Mandangui Maloumbi, Réolie Foxie Mizélé Kitoti, and Dominique Mizère. "The New Bivariate Conway-Maxwell-Poisson Distribution Obtained by the Crossing Method." International Journal of Statistics and Probability 9, no. 6 (September 13, 2020): 1. http://dx.doi.org/10.5539/ijsp.v9n6p1.

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Kimberly et al. had proposed in 2016 a bivariate function as a bivariate Conway-Maxwell-Poisson distribution (COM-Poisson) using the generalized bivariate Poisson distribution and the probability generating functions of the follow distributions: bivariate bernoulli, bivariate Poisson, bivariate geometric and bivariate binomial. By examining this paper we have shown that this bivariate function is constant and it double series is divergent, when it should have been 1. To overcome this deadlock, we propose a new bivariate Conway-Maxwell-Poisson distribution which is definetely a probability distribution based on the crossing method, method highlighted by Elion et al. in 2016 and revisited by Batsindila et al. and Mandangui et al. in 2019. And this is the purpose of this paper. To this bivariate distribution is attached two generalized linear models (GLM) whose resolution allows us to highlight, not only the independence between the variables forming the couple, but also the effect of the factors (or predictors) on these variables. The resulting correlation is negative, zero or positive depending on the values of a parameter; in particular for the bivariate Poisson distribution according to Berkhout and Plug. A simulation of data will be given at the end of the article to illustrate the model.

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Li, Shuanming, and José Garrido. "Ruin Probabilities for Two Classes of Risk Processes." ASTIN Bulletin 35, no. 01 (May 2005): 61–77. http://dx.doi.org/10.2143/ast.35.1.583166.

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We consider a risk model with two independent classes of insurance risks. We assume that the two independent claim counting processes are, respectively, Poisson and Sparre Andersen processes with generalized Erlang(2) claim inter-arrival times. The Laplace transform of the non-ruin probability is derived from a system of integro-differential equations. Explicit results can be obtained when the initial reserve is zero and the claim severity distributions of both classes belong to the Kn family of distributions. A relation between the ruin probability and the distribution of the supremum before ruin is identified. Finally, the Laplace transform of the non-ruin probability of a perturbed Sparre Andersen risk model with generalized Erlang(2) claim inter-arrival times is derived when the compound Poisson process converges weakly to a Wiener process.

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Li, Shuanming, and José Garrido. "Ruin Probabilities for Two Classes of Risk Processes." ASTIN Bulletin 35, no. 1 (May 2005): 61–77. http://dx.doi.org/10.1017/s0515036100014069.

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We consider a risk model with two independent classes of insurance risks. We assume that the two independent claim counting processes are, respectively, Poisson and Sparre Andersen processes with generalized Erlang(2) claim inter-arrival times. The Laplace transform of the non-ruin probability is derived from a system of integro-differential equations. Explicit results can be obtained when the initial reserve is zero and the claim severity distributions of both classes belong to the Kn family of distributions. A relation between the ruin probability and the distribution of the supremum before ruin is identified. Finally, the Laplace transform of the non-ruin probability of a perturbed Sparre Andersen risk model with generalized Erlang(2) claim inter-arrival times is derived when the compound Poisson process converges weakly to a Wiener process.

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Draney, Michael L., Jason C. Doll, Lydia R. Doerr, Christopher J. Houghton, and Patrick S. Forsythe. "Spatial Distribution of a Tree Trunk Specialist Spider: Relative Role of Landscape Versus Microhabitat Drivers." Environmental Entomology 49, no. 4 (May 20, 2020): 963–73. http://dx.doi.org/10.1093/ee/nvaa051.

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Abstract By completely censusing a 1 ha forest dynamics plot it was possible to identify the variables (spider mass, size, sex and tree species, size, and bark roughness) that influenced the spatial distribution of adult Drapetisca alteranda Chamberlin 1909 (Araneae: Linyphiidae), a sheet web spider that specializes in lower tree trunks in North American forests. To account for spatial autocorrelation, a conditional autoregressive random effect was included in the zero-inflated Poisson generalized linear mixed model. Parameters estimated were produced by Bayesian inference with vague prior probability distributions and the best of 16 models were selected using Watanabe-Akaike Information Criterion. The best model showed that larger diameter trees located at higher plot elevations were more likely to have D. alteranda present. Smooth bark tree species such as paper birch and American basswood tended to have the most spiders while rough bark species had the least. The relationship between tree diameter and D. alteranda abundance also varied by tree species. Paper birch and quaking aspen tend to produce a greater slope compared to the other species, indicating that as these trees get larger, the abundance of D. alteranda increases at a higher rate than on other tree species. Spider sex and size were not associated with height on the trunk or tree species selection, nor were they associated with microhabitats such as bark furrow depth. Landscape-level factors largely predict D. alteranda abundance and distribution, suggesting that spatial autocorrelation should be considered when modeling the abundance of even small organisms, such as spiders.

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Arévalo-Támara, Andrea, Mauricio Orozco-Fontalvo, and Víctor Cantillo. "Factors Influencing Crash Frequency on Colombian Rural Roads." Promet - Traffic&Transportation 32, no. 4 (July 9, 2020): 449–60. http://dx.doi.org/10.7307/ptt.v32i4.3385.

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Traffic crashes in Colombia have become a public health problem causing about 7,000 deaths and 45,000 severe injuries per year. Around 40% of these events occur on rural roads, taking note that the vulnerable users (pedestrians, motorcyclists, cyclists) account for the largest percentage of the victims. The objective of this research is to identify the factors that influence the frequency of crashes, including the singular orography of the country. For this purpose, we estimated Negative Binomial (Poisson-gamma) regression, Zero-inflated model, and generalized the linear mixed model, thus developing a comparative analysis of results in the Colombian context. The data used in the study came from the official sources regarding records about crashes with consequences; that is, with the occurrence of fatalities or injuries on the Colombian roads. For collecting the highway characteristics, an in-field inventory was conducted, gathering information about both infrastructure and operational parameters in more than three thousand kilometres of the national network. The events were geo-referenced, with registries of vehicles, involved victims, and their condition. The results suggest that highways in flat terrain have higher crash frequency than highways in rolling or mountainous terrain. Besides, the presence of pedestrians, the existence of a median and the density of intersections per kilometre also increase the probability of crashes. Meanwhile, roads with shoulders and wide lanes have lower crash frequency. Specific interventions in the infrastructure and control for reducing crashes risk attending the modelling results have been suggested.

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Ma, Zonggang, Chaoqun Ma, and Shisong Xiao. "Pricing Zero-Coupon Catastrophe Bonds Using EVT with Doubly Stochastic Poisson Arrivals." Discrete Dynamics in Nature and Society 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/3279647.

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The frequency and severity of climate abnormal change displays an irregular upward cycle as global warming intensifies. Therefore, this paper employs a doubly stochastic Poisson process with Black Derman Toy (BDT) intensity to describe the catastrophic characteristics. By using the Property Claim Services (PCS) loss index data from 2001 to 2010 provided by the US Insurance Services Office (ISO), the empirical result reveals that the BDT arrival rate process is superior to the nonhomogeneous Poisson and lognormal intensity process due to its smaller RMSE, MAE, MRPE, and U and larger E and d. Secondly, to depict extreme features of catastrophic risks, this paper adopts the Peak Over Threshold (POT) in extreme value theory (EVT) to characterize the tail characteristics of catastrophic loss distribution. And then the loss distribution is analyzed and assessed using a quantile-quantile (QQ) plot to visually check whether the PCS index observations meet the generalized Pareto distribution (GPD) assumption. Furthermore, this paper derives a pricing formula for zero-coupon catastrophe bonds with a stochastic interest rate environment and aggregate losses generated by a compound doubly stochastic Poisson process under the forward measure. Finally, simulation results verify pricing model predictions and show how catastrophic risks and interest rate risk affect the prices of zero-coupon catastrophe bonds.

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Famoye, Felix, and John S. Preisser. "Marginalized zero-inflated generalized Poisson regression." Journal of Applied Statistics 45, no. 7 (August 16, 2017): 1247–59. http://dx.doi.org/10.1080/02664763.2017.1364717.

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Zamani, Hossein, and Noriszura Ismail. "Score test for testing zero-inflated Poisson regression against zero-inflated generalized Poisson alternatives." Journal of Applied Statistics 40, no. 9 (September 2013): 2056–68. http://dx.doi.org/10.1080/02664763.2013.804904.

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Tanghöj, Gustaf, Petru Liuba, Gunnar Sjöberg, Annika Rydberg, and Estelle Naumburg. "Adverse events within 1 year after surgical and percutaneous closure of atrial septal defects in preterm children." Cardiology in the Young 29, no. 5 (May 2019): 626–36. http://dx.doi.org/10.1017/s1047951119000350.

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AbstractIntroduction:Atrial septal defect is the third most common CHD. A hemodynamically significant atrial septal defect causes volume overload of the right side of the heart. Preterm children may suffer from both pulmonary and cardiac comorbidities, including altered myocardial function. The aim of this study was to compare the rate of adverse events following atrial septal defect closure in preterm- and term-born children.Method:We performed a retrospective cohort study including children born in Sweden, who had a surgical or percutaneous atrial septal defect closure at the children’s hospitals in Lund and Stockholm, between 2000 and 2014, assessing time to the first event within 1 month or 1 year. We analysed differences in the number of and the time to events between the preterm and term cohort using the Kaplan–Meier survival curve, a generalised model applying zero-inflated Poisson distribution and Gary-Anderson’s method.Results:Overall, 413 children were included in the study. Of these, 93 (22.5%) were born prematurely. The total number of adverse events was 178 (110 minor and 68 major). There was no difference between the cohorts in the number of events, whether within 1 month or within a year, between major (p = 0.69) and minor (p = 0.84) events or frequencies of multiple events (p = 0.92).Conclusion:Despite earlier procedural age, larger atrial septal defects, and higher comorbidity than term children, preterm children appear to have comparable risk for complications during the first year after surgical or percutaneous closure.

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DASSIOS, ANGELOS, and HONGBIAO ZHAO. "A GENERALIZED CONTAGION PROCESS WITH AN APPLICATION TO CREDIT RISK." International Journal of Theoretical and Applied Finance 20, no. 01 (February 2017): 1750003. http://dx.doi.org/10.1142/s0219024917500030.

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We introduce a class of analytically tractable jump processes with contagion effects by generalizing the classical Hawkes process. This model framework combines the characteristics of three popular point processes in the literature: (1) Cox process with CIR intensity; (2) Cox process with Poisson shot-noise intensity; (3) Hawkes process with exponentially decaying intensity. Hence, it can be considered as a self-exciting and externally-exciting point process with mean-reverting stochastic intensity. Essential probabilistic properties such as moments, the Laplace transform of intensity process, and the probability generating function of point process as well as some important asymptotics have been derived. Some special cases and a method for change of measure are discussed. This point process may be applicable to modeling contagious arrivals of events for various circumstances (such as jumps, transactions, losses, defaults, catastrophes) in finance, insurance and economics with both endogenous and exogenous risk factors within one framework. More specifically, these exogenous factors could contain relatively short-lived shocks and long-lasting risk drivers. We make a simple application to calculate the default probability for credit risk and to price defaultable zero-coupon bonds.

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Negrisoli, Juliana, and Luiz Fernando C. Nascimento. "Atmospheric pollutants and hospital admissions due to pneumonia in children." Revista Paulista de Pediatria 31, no. 4 (December 2013): 501–6. http://dx.doi.org/10.1590/s0103-05822013000400013.

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OBJECTIVE: To analyze the relationship between exposure to air pollutants and hospitalizations due to pneumonia in children of Sorocaba, São Paulo, Brazil. METHODS: Time series ecological study, from 2007 to 2008. Daily data were obtained from the State Environmental Agency for Pollution Control for particulate matter, nitric oxide, nitrogen dioxide, ozone, besides air temperature and relative humidity. The data concerning pneumonia admissions were collected in the public health system of Sorocaba. Correlations between the variables of interest using Pearson cofficient were calculated. Models with lags from zero to five days after exposure to pollutants were performed to analyze the association between the exposure to environmental pollutants and hospital admissions. The analysis used the generalized linear model of Poisson regression, being significant p<0.05. RESULTS: There were 1,825 admissions for pneumonia, with a daily mean of 2.5±2.1. There was a strong correlation between pollutants and hospital admissions, except for ozone. Regarding the Poisson regression analysis with the multi-pollutant model, only nitrogen dioxide was statistically significant in the same day (relative risk - RR=1.016), as well as particulate matter with a lag of four days (RR=1.009) after exposure to pollutants. CONCLUSIONS: There was an acute effect of exposure to nitrogen dioxide and a later effect of exposure to particulate matter on children hospitalizations for pneumonia in Sorocaba.

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Sehgal, Meena, and Sujit Ghosh. "Exploring the Usefulness of Meteorological Data for Predicting Malaria Cases in Visakhapatnam, Andhra Pradesh." Weather, Climate, and Society 12, no. 2 (April 2020): 323–30. http://dx.doi.org/10.1175/wcas-d-19-0029.1.

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AbstractMalaria and dengue fever are among the most important vectorborne diseases in the tropics and subtropics. Average weekly meteorological parameters—specifically, minimum temperature, maximum temperature, humidity, and rainfall—were collected using data from 100 automated weather stations from the Indian Space Research Organization. We obtained district-level weekly reported malaria cases from the Integrated Disease Surveillance Program (IDSP), Department of Health and Family Welfare, Andhra Pradesh, India, for three years, 2014–16. We used a generalized linear model with Poisson distribution and default logarithm-link to estimate model parameters, and we used a quasi-Poisson method with a generalized additive model that uses nonparametric regression with smoothing splines. It appears that higher minimum temperatures (e.g., >24°C) tend to lead to higher malaria counts but lower values do not seem to have an impact on the malaria counts. On the other hand, higher values of maximum temperature (e.g., >32°C) seem to negatively affect the malaria counts. The relationships with rainfall and humidity appear to be not as strong once we account for smooth (weekly) trends and temperatures; both smooth curves seem to hover around zero across all of their values. We note that a rainfall amount between 40 and 50 mm seems to have a positive impact on malaria counts. Our analyses show that the incremental increase in meteorological parameters does not lead to an increase in reported malaria cases in the same manner for all of the districts within the same state. This suggests that other factors such as vegetation, elevation, and water index in the environment also influence disease occurrence.

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Journal articles on the topic 'Zero-inflated generalized Poisson model'

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