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Relevant bibliographies by topics / Zero-inflated distribution

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Academic literature on the topic 'Zero-inflated distribution'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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Journal articles on the topic "Zero-inflated distribution"

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Wagh, Yogita S., and Kirtee K. Kamalja. "Zero-inflated models and estimation in zero-inflated Poisson distribution." Communications in Statistics - Simulation and Computation 47, no. 8 (August 4, 2017): 2248–65. http://dx.doi.org/10.1080/03610918.2017.1341526.

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Krishna, Patil Maruti, and Shirke Digambar Tukaram. "Bivariate Zero-Inflated Power Series Distribution." Applied Mathematics 02, no. 07 (2011): 824–29. http://dx.doi.org/10.4236/am.2011.27110.

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Nanjundan, G., and Sadiq Pasha. "Characterization of Zero-inflated Gamma Distribution." Journal of Computer and Mathematical Sciences 9, no. 12 (December 4, 2018): 1861–65. http://dx.doi.org/10.29055/jcms/932.

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Brobbey, Anita, Aerambamoorthy Thavaneswaran, and Saumen Mandal. "Wrapped Zero-inflated Poisson Distribution and Its Properties." International Journal of Statistics and Probability 5, no. 1 (December 24, 2015): 111. http://dx.doi.org/10.5539/ijsp.v5n1p111.

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Recently, there has been a growing interest in discrete valued wrapped distributions and the trigonometric moments.<br />Characteristic functions of stable processes have been used to study the estimation of the model parameters using<br />estimating function approach (Thavaneswaran et al., 2013). In this paper, we introduce a new discrete circular distribution,<br />the wrapped zero-inflated Poisson distribution and derive its population characteristics.<br /><br />

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文, 静蕊. "Variable Selection of Zero-Inflated Geometric Distribution." Advances in Applied Mathematics 10, no. 04 (2021): 1243–54. http://dx.doi.org/10.12677/aam.2021.104135.

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Thas, Olivier, and J. C. W. Rayner. "Smooth Tests for the Zero-Inflated Poisson Distribution." Biometrics 61, no. 3 (May 3, 2005): 808–15. http://dx.doi.org/10.1111/j.1541-0420.2005.00351.x.

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Kolev, Nikolai, and Ljuben Mutafchiev. "A zero-inflated occupancy distribution: exact results and Poisson convergence." International Journal of Mathematics and Mathematical Sciences 2003, no. 28 (2003): 1771–82. http://dx.doi.org/10.1155/s0161171203209017.

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We introduce the generalized zero-inflated allocation scheme of placingnlabeled balls intoNlabeled cells. We study the asymptotic behavior of the number of empty cells when(n,N)belongs to the right and left domain of attraction. An application to the estimation of characteristics of agreement among a set of raters which independently classify subjects into one of two categories is also indicated. The case when a large number of raters acts following the zero-inflated binomial law with small probability for positive diagnosis is treated using the zero-inflated Poisson approximation.

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Constantinescu, Corina D., Tomasz J. Kozubowski, and Haoyu H. Qian. "Probability of ruin in discrete insurance risk model with dependent Pareto claims." Dependence Modeling 7, no. 1 (July 11, 2019): 215–33. http://dx.doi.org/10.1515/demo-2019-0011.

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AbstractWe present basic properties and discuss potential insurance applications of a new class of probability distributions on positive integers with power law tails. The distributions in this class are zero-inflated discrete counterparts of the Pareto distribution. In particular, we obtain the probability of ruin in the compound binomial risk model where the claims are zero-inflated discrete Pareto distributed and correlated by mixture.

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Park, Seong-min, and Bonnie S. Fisher. "Understanding the Effect of Immunity on Over-Dispersed Criminal Victimizations: Zero-Inflated Analysis of Household Victimizations in the NCVS." Crime & Delinquency 63, no. 9 (October 6, 2015): 1116–45. http://dx.doi.org/10.1177/0011128715607534.

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This study aims to empirically test the immunity effect on the frequency distribution of household victimizations. To clarify the immunity effect, the statistical construction of zero-inflated models is reviewed and compared with that of non-zero-inflated models. The Benjamini and Hochberg correction is used to address the limitation of p values in multiple testing. Compared with the findings from the non-zero-inflated model, two sets of coefficients from the zero-inflated model reveal that there exist more complex and diverse statuses in the process of household victimization than predicted by risk heterogeneity and event dependence. With these findings, this study suggests that zero-inflated models should be introduced and compared with non-zero-inflated models for the clarification of victimization determinants.

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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 "Zero-inflated distribution"

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Wan, Chung-him, and 溫仲謙. "Analysis of zero-inflated count data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43703719.

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Wan, Chung-him. "Analysis of zero-inflated count data." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43703719.

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Dai, Xiaogang. "Score Test and Likelihood Ratio Test for Zero-Inflated Binomial Distribution and Geometric Distribution." TopSCHOLAR®, 2018. https://digitalcommons.wku.edu/theses/2447.

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The main purpose of this thesis is to compare the performance of the score test and the likelihood ratio test by computing type I errors and type II errors when the tests are applied to the geometric distribution and inflated binomial distribution. We first derive test statistics of the score test and the likelihood ratio test for both distributions. We then use the software package R to perform a simulation to study the behavior of the two tests. We derive the R codes to calculate the two types of error for each distribution. We create lots of samples to approximate the likelihood of type I error and type II error by changing the values of parameters. In the first chapter, we discuss the motivation behind the work presented in this thesis. Also, we introduce the definitions used throughout the paper. In the second chapter, we derive test statistics for the likelihood ratio test and the score test for the geometric distribution. For the score test, we consider the score test using both the observed information matrix and the expected information matrix, and obtain the score test statistic zO and zI . Chapter 3 discusses the likelihood ratio test and the score test for the inflated binomial distribution. The main parameter of interest is w, so p is a nuisance parameter in this case. We derive the likelihood ratio test statistics and the score test statistics to test w. In both tests, the nuisance parameter p is estimated using maximum likelihood estimator pˆ. We also consider the score test using both the observed and the expected information matrices. Chapter 4 focuses on the score test in the inflated binomial distribution. We generate data to follow the zero inflated binomial distribution by using the package R. We plot the graph of the ratio of the two score test statistics for the sample data, zI /zO , in terms of different values of n0, the number of zero values in the sample. In chapter 5, we discuss and compare the use of the score test using two types of information matrices. We perform a simulation study to estimate the two types of errors when applying the test to the geometric distribution and the inflated binomial distribution. We plot the percentage of the two errors by fixing different parameters, such as the probability p and the number of trials m. Finally, we conclude by briefly summarizing the results in chapter 6.

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Pailden, Junvie Montealto. "Applications of Empirical Likelihood to Zero-Inflated Data and Epidemic Change Point." Bowling Green State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1367579613.

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Fan, Huihao. "Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407404513.

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Ibukun, Michael Abimbola. "Modely s Touchardovým rozdělením." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445468.

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In 2018, Raul Matsushita, Donald Pianto, Bernardo B. De Andrade, Andre Cançado & Sergio Da Silva published a paper titled ”Touchard distribution”, which presented a model that is a two-parameter extension of the Poisson distribution. This model has its normalizing constant related to the Touchard polynomials, hence the name of this model. This diploma thesis is concerned with the properties of the Touchard distribution for which delta is known. Two asymptotic tests based on two different statistics were carried out for comparison in a Touchard model with two independent samples, supported by simulations in R.

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Silva, João Flávio Andrade. "Modelos preditivos para LGD." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/104/104131/tde-13112018-084000/.

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As instituições financeiras que pretendem utilizar a IRB (Internal Ratings Based) avançada precisam desenvolver métodos para estimar a componente de risco LGD (Loss Given Default). Desde a década de 1950 são apresentadas propostas para modelagem da PD (Probability of default), em contrapartida, a previsão da LGD somente recebeu maior atenção após a publicação do Acordo Basileia II. A LGD possui ainda uma literatura pequena, se comparada a PD, e não há um método eficiente em termos de acurácia e interpretação como é a regressão logística para a PD. Modelos de regressão para LGD desempenham um papel fundamental na gestão de risco das instituições financeiras. Devido sua importância este trabalho propõe uma metodologia para quantificar a componente de risco LGD. Considerando as características relatadas sobre a distribuição da LGD e na forma flexível que a distribuição beta pode assumir, propomos uma metodologia de estimação da LGD por meio do modelo de regressão beta bimodal inflacionado em zero. Desenvolvemos a distribuição beta bimodal inflacionada em zero, apresentamos algumas propriedades, incluindo momentos, definimos estimadores via máxima verossimilhança e construímos o modelo de regressão para este modelo probabilístico, apresentamos intervalos de confiança assintóticos e teste de hipóteses para este modelo, bem como critérios para seleção de modelos, realizamos um estudo de simulação para avaliar o desempenho dos estimadores de máxima verossimilhança para os parâmetros da distribuição beta bimodal inflacionada em zero. Para comparação com nossa proposta selecionamos os modelos de regressão beta e regressão beta inflacionada, que são abordagens mais usuais, e o algoritmo SVR , devido a significativa superioridade relatada em outros trabalhos.
Financial institutions willing to use the advanced Internal Ratings Based (IRB) need to develop methods to estimate the LGD (Loss Given Default) risk component. Proposals for PD (Probability of default) modeling have been presented since the 1950s, in contrast, LGDs forecast has received more attention only after the publication of the Basel II Accord. LGD also has a small literature, compared to PD, and there is no efficient method in terms of accuracy and interpretation such as logistic regression for PD. Regression models for LGD play a key role in the risk management of financial institutions, due to their importance this work proposes a methodology to quantify the LGD risk component. Considering the characteristics reported on the distribution of LGD and in the flexible form that the beta distribution may assume, we propose a methodology for estimation of LGD using the zero inflated bimodal beta regression model. We developed the zero inflated bimodal beta distribution, presented some properties, including moments, defined estimators via maximum likelihood and constructed the regression model for this probabilistic model, presented asymptotic confidence intervals and hypothesis test for this model, as well as selection criteria of models, we performed a simulation study to evaluate the performance of the maximum likelihood estimators for the parameters of the zero inflated bimodal beta distribution. For comparison with our proposal we selected the beta regression models and inflated beta regression, which are more usual approaches, and the SVR algorithm, due to the significant superiority reported in other studies.

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Zbylut, Joanna. "Modeling proportions to assess the soil nematode community structure in a two year alfalfa crop." Kansas State University, 2014. http://hdl.handle.net/2097/17327.

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Master of Science
Department of Statistics
Leigh Murray
The southern root-knot nematode (SRKN) and the weedy perennials, yellow nutsedge (YNS) and purple nutsedge (PNS) are simultaneously occurring pests in the irrigated agricultural soils of southern New Mexico. Previous research has very well characterized SRKN, YNS and PNS as a mutually-beneficial pest complex and has revealed their enhanced population growth and survival when they occur together. The density of nutsedge in a field could be used as a predictor of SRKN juveniles in the soil. In addition to SRKN, which is the most harmful of the plant parasitic nematodes, in southern New Mexico, other species or categories of nematodes could be identified and counted. Some of them are not as damaging to the plant as SRKN, and some of them may be essential for soil health. The nematode species could be grouped into categories according to trophic level (what nematodes eat) and herbivore feeding behavior (how herbivore nematodes eat). Subsequently, three ratios of counts were calculated for trophic level and for feeding behavior level to investigate the soil nematode community structure. These proportions were modeled as functions of the weed hosts YNS and PNS by generalized linear regression models using the logit link function and three probability distributions: the Binomial, Zero Inflated Binomial (ZIB) and Binomial Hurdle (BH). The latter two were used to account for potential high proportions of zeros in the data. The SAS NLMIXED procedure was used to fit models for each of the six sampling dates (May, July and September) over the two years of the alfalfa study. General results showed that the Binomial pmf generally provided the best fit, indicating lower zero-inflation than expected. Importance of YNS and PNS predictors varied over time and the different ratios. Specific results illustrate the differences in estimated probabilities between Binomial, ZIB and BH distributions as YNS counts increase for two selected ratios.

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Ljung, Carolina, and Maria Svedberg. "Estimation of Loss Given Default Distributions for Non-Performing Loans Using Zero-and-One Inflated Beta Regression Type Models." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273593.

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This thesis investigates three different techniques for estimating loss given default of non-performing consumer loans. This is a contribution to a credit risk evaluation model compliant with the regulations stipulated by the Basel Accords, regulating the capital requirements of European financial institutions. First, multiple linear regression is applied, and thereafter, zero-and-one inflated beta regression is implemented in two versions, with and without Bayesian inference. The model performances confirm that modeling loss given default data is challenging, however, the result shows that the zero-and-one inflated beta regression is superior to the other models in predicting LGD. Although, it shall be recognized that all models had difficulties in distinguishing low-risk loans, while the prediction accuracy of riskier loans, resulting in larger losses, were higher. It is further recommended, in future research, to include macroeconomic variables in the models to capture economic downturn conditions as well as adopting decision trees, for example by applying machine learning.
Detta examensarbete undersöker tre olika metoder för att estimera förlusten vid fallissemang för icke-presterande konsumentlån. Detta som ett bidrag till en kreditrisksmodell i enlighet med bestämmelserna i Baselregelverken, som bland annat reglerar kapitalkraven för europeiska finansiella institut. Inledningsvis tillämpas multipel linjär regression, därefter implementeras två versioner av utvidgad betaregression, med och utan bayesiansk inferens. Resultatet bekräftar att modellering data för förlust givet fallissemang är utmanande, men visar även att den utvidgade betaregressionen utan bayesiansk inferens är bättre de andra modellerna. Det ska dock tilläggas att alla modeller visade svårigheter att estimera lån med låg risk, medan tillförlitligheten hos lån med hög risk, vilka generellt sett medför större förluster, var högre. Vidare rekommenderas det för framtida forskning att inkludera makroekonomiska variabler i modellerna för att fånga ekonomiska nedgångar samt att implementera beslutsträd, exempelvis genom applicering av maskininlärning.

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Silva, Deise Deolindo. "Classe de distribuições série de potências inflacionadas com aplicações." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4536.

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Made available in DSpace on 2016-06-02T20:06:03Z (GMT). No. of bitstreams: 1 2510.pdf: 1878422 bytes, checksum: 882e21e70271b7a106e3a27a080da004 (MD5) Previous issue date: 2009-04-06
This work has as central theme the Inflated Modified Power Series Distributions, where the objective is to study its main properties and the applicability in the bayesian context. This class of models includes the generalized Poisson, binomial and negative binomial distributions. These probability distributions are very helpful to models discrete data with inflated values. As particular case the - zero inflated Poisson models (ZIP) is studied, where the main purpose was to verify the effectiveness of it when compared to the Poisson distribution. The same methodology was considered for the negative binomial inflated distribution, but comparing it with the Poisson, negative binomial and ZIP distributions. The Bayes factor and full bayesian significance test were considered for selecting models.
Este trabalho tem como tema central a classe de distribuições série de potências inflacionadas, em que o intuito é estudar suas principais propriedades e a aplicabilidade no contexto bayesiano. Esta classe de modelos engloba as distribuições de Poisson, binomial e binomial negativa simples e as generalizadas e, por isso é muito aplicada na modelagem de dados discretos com valores excessivos. Como caso particular propôs-se explorar a distribuição de Poisson zero inflacionada (ZIP), em que o objetivo principal foi verificar a eficácia de sua modelagem quando comparada à distribuição de Poisson. A mesma metodologia foi considerada para a distribuição binomial negativa inflacionada, mas comparando-a com as distribuições de Poisson, binomial negativa e ZIP. Como critérios formais para seleção de modelos foram considerados o fator de Bayes e o teste de significância completamente bayesiano.

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Book chapters on the topic "Zero-inflated distribution"

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Bhattacharya, Archan, Bertrand S. Clarke, and Gauri S. Datta. "A Bayesian test for excess zeros in a zero-inflated power series distribution." In Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen, 89–104. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008. http://dx.doi.org/10.1214/193940307000000068.

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Junnumtuam, Sunisa, Sa-Aat Niwitpong, and Suparat Niwitpong. "The Bayesian Confidence Interval for the Mean of the Zero-Inflated Poisson Distribution." In Lecture Notes in Computer Science, 419–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62509-2_35.

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Araldi, Alessandro, Alessandro Venerandi, and Giovanni Fusco. "Count Regression and Machine Learning Approach for Zero-Inflated Over-Dispersed Count Data. Application to Micro-Retail Distribution and Urban Form." In Computational Science and Its Applications – ICCSA 2020, 550–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58811-3_40.

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Yee, Thomas W. "Zero-Inflated, Zero-Altered and Positive Discrete Distributions." In Springer Series in Statistics, 469–97. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2818-7_17.

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Palazzo, Lucio, Pietro Sabatino, and Riccardo Ievoli. "Determinants of social startups in Italy." In Proceedings e report, 85–90. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.18.

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The so called "Startup Act" (Decree Law 179/2012, converted into Law 221/2012), has introduced in Italy the notion of innovative companies with a high technological value, denoted as the innovative startups. Among them, the Italian government includes the category of SIAVS ("Startup Innovative A Vocazione Sociale"), which represents a relatively new field of interest in both scientific and normative perspective. A social startup must satisfy the same requirement of other innovative startups, usually operating in sectors such as social assistance, education, health, social tourism and culture which can have a direct (social) impact on collective well-being. Furthermore, they must produce specific reporting of the produced social impact, enjoying also some tax benefits. In 2020 more than 200 SIAVS are registered in Italy, more than doubled with respect to 2015. This work is concerned with the empirical analysis of innovative companies focused in funding and implementing solutions to social, cultural, or environmental issues. Specifically, the aim of the paper is to investigate what are the relevant factors for the arise of SIAVS in Italy. The response variable is based on the number of active social startups in Italian provinces while the set of explanatory variables is composed by economic and demographic indicators at the provincial level. Generalized linear models (GLM) for discrete outcomes are applied and compared, even taking into account the zero-inflated issue arising due to the distribution of these particular data.

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Tse, Siu, Shein Chow, and Qingshu Lu. "Zero-Inflated Poisson Distribution." In Encyclopedia of Biopharmaceutical Statistics, Third Edition, 1436–42. CRC Press, 2012. http://dx.doi.org/10.1201/b14674-232.

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Tse, Siu Keung, Shein Chung Chow, and Qingshu Lu. "Zero-Inflated Poisson Distribution." In Encyclopedia of Biopharmaceutical Statistics, 1436–42. Informa Healthcare, 2010. http://dx.doi.org/10.3109/9781439822463.230.

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Stewart Sparks, Ross, and Hossein Hazrati-Marangaloo. "Exponentially Weighted Moving Averages of Counting Processes When the Time between Events Is Weibull Distributed." In Quality Control in Intelligent Manufacturing [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.90873.

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There are control charts for Poisson counts, zero-inflated Poisson counts, and over dispersed Poisson counts (negative binomial counts) but nothing on counting processes when the time between events (TBEs) is Weibull distributed. In our experience the in-control distribution for time between events is often Weibull distributed in applications. Counting processes are not Poisson distributed or negative binomial distributed when the time between events is Weibull distributed. This is a gap in the literature meaning that there is no help for practitioners when this is the case. This book chapter is designed to close this gap and provide an approach that could be helpful to those applying control charts in such cases.

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"3 INFLATED ZERO DISTRIBUTIONS." In Truncated and Censored Samples, 258. CRC Press, 2016. http://dx.doi.org/10.1201/b16946-109.

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Ahmad, Peer Bilal. "Bayesian Analysis of Zero-Inflated Generalized Power Series Distributions Under Different Loss Functions." In Bayesian Analysis and Reliability Estimation of Generalized Probability Distributions, 1–12. AIJR Publisher, 2019. http://dx.doi.org/10.21467/books.44.1.

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Conference papers on the topic "Zero-inflated distribution"

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Yamrubboon, Darika, Ampai Thongteeraparp, Winai Bodhisuwan, and Katechan Jampachaisri. "Zero inflated negative binomial-Sushila distribution and its application." In PROCEEDINGS OF THE 13TH IMT-GT INTERNATIONAL CONFERENCE ON MATHEMATICS, STATISTICS AND THEIR APPLICATIONS (ICMSA2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5012263.

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Economou, T., Z. Kapelan, and T. Bailey. "A Zero-Inflated Bayesian Model for the Prediction of Water Pipe Bursts." In Water Distribution Systems Analysis 2008. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41024(340)61.

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Sirichantra, Chutima, and Winai Bodhisuwan. "Parameter estimation of the zero inflated negative binomial beta exponential distribution." In PROCEEDINGS OF THE 13TH IMT-GT INTERNATIONAL CONFERENCE ON MATHEMATICS, STATISTICS AND THEIR APPLICATIONS (ICMSA2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5012260.

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Kong, Shufeng, Junwen Bai, Jae Hee Lee, Di Chen, Andrew Allyn, Michelle Stuart, Malin Pinsky, Katherine Mills, and Carla Gomes. "Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/603.

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A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of species have to be simultaneously modeled and (ii) the survey data are usually inflated with zeros due to the absence of species for a large number of sites. The problem of tackling both issues simultaneously, which we refer to as the zero-inflated multi-target regression problem, has not been addressed by previous methods in statistics and machine learning. In this paper, we propose a novel deep model for the zero-inflated multi-target regression problem. To this end, we first model the joint distribution of multiple response variables as a multivariate probit model and then couple the positive outcomes with a multivariate log-normal distribution. By penalizing the difference between the two distributions’ covariance matrices, a link between both distributions is established. The whole model is cast as an end-to-end learning framework and we provide an efficient learning algorithm for our model that can be fully implemented on GPUs. We show that our model outperforms the existing state-of-the-art baselines on two challenging real-world species distribution datasets concerning bird and fish populations.

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Kaviyarasu, V., and A. Parimala. "Evaluation of Bayesian quick switching single sampling system using Gamma zero inflated Poisson distribution." In PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ADVANCES IN MATERIALS RESEARCH (ICAMR - 2019). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0017657.

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Nishii, Ryuei, and Shojiro Tanaka. "An application of novel zero-one inflated distributions with spatial dependence for the deforestation modeling." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5654397.

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Trashchenkov, Sergei, and Victor Astapov. "The applicability of zero inflated beta distributions for stochastic modeling of PV plants' power output." In 2018 19th International Scientific Conference on Electric Power Engineering (EPE). IEEE, 2018. http://dx.doi.org/10.1109/epe.2018.8395965.

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Jaithun, Maneerat, and Manad Khamkong. "Optimal parameter estimation for zero–inflated gamma distributions with application to rainfall data of Yom River in Northern Thailand." In PROCEEDINGS OF THE 13TH IMT-GT INTERNATIONAL CONFERENCE ON MATHEMATICS, STATISTICS AND THEIR APPLICATIONS (ICMSA2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5012240.

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