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Journal articles on the topic 'Zero-inflated distribution'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

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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3

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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6

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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DENWOOD, M. J., M. J. STEAR, L. MATTHEWS, S. W. J. REID, N. TOFT, and G. T. INNOCENT. "The distribution of the pathogenic nematodeNematodirus battusin lambs is zero-inflated." Parasitology 135, no. 10 (July 14, 2008): 1225–35. http://dx.doi.org/10.1017/s0031182008004708.

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SUMMARYUnderstanding the frequency distribution of parasites and parasite stages among hosts is essential for efficient experimental design and statistical analysis, and is also required for the development of sustainable methods of controlling infection.Nematodirus battusis one of the most important organisms that infect sheep but the distribution of parasites among hosts is unknown. An initial analysis indicated a high frequency of animals withoutN. battusand with zero egg counts, suggesting the possibility of a zero-inflated distribution. We developed a Bayesian analysis using Markov chain Monte Carlo methods to estimate the parameters of the zero-inflated negative binomial distribution. The analysis of 3000 simulated data sets indicated that this method out-performed the maximum likelihood procedure. Application of this technique to faecal egg counts from lambs in a commercial upland flock indicated thatN. battuscounts were indeed zero-inflated. Estimating the extent of zero-inflation is important for effective statistical analysis and for the accurate identification of genetically resistant animals.

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Suresh, R., G. Nanjundan, S. Nagesh, and Sadiq Pasha. "On a Characterization of Zero-Inflated Negative Binomial Distribution." Open Journal of Statistics 05, no. 06 (2015): 511–13. http://dx.doi.org/10.4236/ojs.2015.56053.

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Sakthivel, K. M., and C. S. Rajitha. "ZERO-INFLATED NEGATIVE BINOMIAL-LINDLEY DISTRIBUTION AND ITS APPLICATION." Far East Journal of Theoretical Statistics 55, no. 2 (March 10, 2019): 101–12. http://dx.doi.org/10.17654/ts055020101.

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14

Monod, Anthea. "Random Effects Modeling and the Zero-Inflated Poisson Distribution." Communications in Statistics - Theory and Methods 43, no. 4 (February 2014): 664–80. http://dx.doi.org/10.1080/03610926.2013.814782.

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Liu, Yin, and Guo-Liang Tian. "Type I multivariate zero-inflated Poisson distribution with applications." Computational Statistics & Data Analysis 83 (March 2015): 200–222. http://dx.doi.org/10.1016/j.csda.2014.10.010.

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Chen, Nan, Shiyu Zhou, Tzyy-Shuh Chang, and Howard Huang. "Attribute control charts using generalized zero-inflated Poisson distribution." Quality and Reliability Engineering International 24, no. 7 (June 2, 2008): 793–806. http://dx.doi.org/10.1002/qre.928.

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Fatimata, LO, BA Demba Bocar, and DIOP Aba. "ASYMPTOTIC PROPERTIES IN THE PROBIT-ZERO-INFLATED BINOMIAL REGRESSION MODEL." Journal of Computer Science and Applied Mathematics 3, no. 2 (September 18, 2021): 68–81. http://dx.doi.org/10.37418/jcsam.3.2.3.

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Zero-inflated regression models have had wide application recently and have provenuseful in modeling data with many zeros. Zero-inflated Binomial (ZIB) regression model is an extension of the ordinary binomial distribution that takes into account the excess of zeros. In comparing the probit model to the logistic model, many authors believe that there is little theoretical justification in choosing one formulation over the other in most circumstances involving binary responses. The logit model is considered to be computationally simpler but it is based on a more restrictive assumption of error independence, although many other generalizations have dealt with that assumption as well. By contrast, the probit model assumes that random errors have a multivariate normal distribution. This assumption makes the probit model attractive because the normal distribution provides a good approximation to many other distributions. In this paper, we develop a maximum likelihood estimation procedure for the parameters of a zero-inflated Binomial regression model with probit link function for both component of the model. We establish the existency, consistency and asymptotic normality of the proposed estimator.

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Abe, Hiroyasu, and Hiroshi Yadohisa. "AUTOMATIC RELEVANCE DETERMINATION IN NONNEGATIVE MATRIX FACTORIZATION BASED ON A ZERO-INFLATED COMPOUND POISSON-GAMMA DISTRIBUTION." Journal of the Japanese Society of Computational Statistics 29, no. 1 (2016): 29–54. http://dx.doi.org/10.5183/jjscs.1608001_233.

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19

Huang, Xi-Fen, Guo-Liang Tian, Chi Zhang, and Xuejun Jiang. "Type I multivariate zero-inflated generalized Poisson distribution with applications." Statistics and Its Interface 10, no. 2 (2017): 291–311. http://dx.doi.org/10.4310/sii.2017.v10.n2.a12.

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Gilles, Rodica, and Seik Kim. "Distribution-free estimation of zero-inflated models with unobserved heterogeneity." Statistical Methods in Medical Research 26, no. 3 (June 24, 2015): 1532–42. http://dx.doi.org/10.1177/0962280215588940.

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This paper presents a quasi-conditional likelihood method for the consistent estimation of both continuous and count data models with excess zeros and unobserved individual heterogeneity when the true data generating process is unknown. Monte Carlo simulation studies show that our zero-inflated quasi-conditional maximum likelihood (ZI-QCML) estimator outperforms other methods and is robust to distributional misspecifications. We apply the ZI-QCML estimator to analyze the frequency of doctor visits.

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21

Bansal, Ashok K., and Priyanka Aggarwal. "Bayesian Estimation for the Zero Inflated Modified Power Series Distribution." American Journal of Mathematical and Management Sciences 27, no. 1-2 (January 2007): 5–23. http://dx.doi.org/10.1080/01966324.2007.10737685.

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22

Huang, Daozheng, Hao Hu, and Yizhou Li. "Zero-Inflated Exponential Distribution of Casualty Rate in Ship Collision." Journal of Shanghai Jiaotong University (Science) 24, no. 6 (September 30, 2019): 739–44. http://dx.doi.org/10.1007/s12204-019-2121-3.

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23

Bodhisuwan, Rujira, and Adam Kehler. "The Zero-inflated Negative Binomial-Exponential Distribution and Its Application." Lobachevskii Journal of Mathematics 42, no. 2 (February 2021): 300–307. http://dx.doi.org/10.1134/s1995080221020062.

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24

Pho, Kim-Hung, and Buu-Chau Truong. "Comparison of the Performance of the Gradient and Newton-Raphson Method to Estimate Parameters in Some Zero-Inflated Regression Models." Journal of Advanced Engineering and Computation 4, no. 4 (December 31, 2020): 227. http://dx.doi.org/10.25073/jaec.202044.297.

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This paper compares the performance of the gradient and Newton-Raphson (N-R) method to estimate parameters in some zero-inflated (ZI) regression models such as the zero-inflated Poisson (ZIP) model, zero-inflated Bell (ZIBell) model, zero-inflated binomial (ZIB) model and zero-inflated negative binomial (ZINB) model. In the present work, firstly, we briefly present the approach of the gradient and N-R method. We then introduce the origin, formulas and applications of the ZI models. Finally, we compare the performance of two investigated approaches for these models through the simulation studies with numerous sample sizes and several missing rates. A real data set is investigated in this study. Specifically, we compare the results and the execution time of the R code for two methods. Moreover, we provide some important notes on these two approaches and some scalable research directions for future work.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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Nagy, Dylan J., David M. Dziewulski, Neculai Codru, and Ursula L. Lauper. "Understanding the distribution of positive Legionella samples in healthcare-premise water systems: Using statistical analysis to determine a distribution for Legionella and to support sample size recommendations." Infection Control & Hospital Epidemiology 42, no. 1 (October 8, 2020): 63–68. http://dx.doi.org/10.1017/ice.2020.384.

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AbstractObjective:To significantly fit a statistical distribution to the proportion of positive Legionella samples in a series of water samples from multiple facility-premise water systems.Design:Statistical fit test.Setting:A hospital and associated long-term care facility (LTCF) in New York State, as well as temporal and culture data from a deidentified hospital site supplied by one of the vendor laboratories.Methods:Culture samples (n = 1,393) were segmented into 139 test cycles with roughly 10 samples in each. The proportion of positive samples was standardized to 25 total samples per test to give a distribution of discrete values. These values were analyzed for fit with the following discrete distributions: Poisson, negative binomial, geometric, and zero-inflated Poisson.Results:The zero-inflated Poisson distribution fitted to the copper–silver ionization (CSI)-treated and untreated test cycles indicates that 88% of the expected positive proportions should occur by the 30% cutoff (rounded up to 8 positive samples among 25 total samples), similar to the 93% expectation for just CSI-treated test cycles. The other treatment in these data (chlorine dioxide) was not effective in treating Legionella in the sampled buildings, and if there is an underlying distribution to these specific test cycles, it is not the zero-inflated Poisson distribution.Conclusions:In a well-maintained or well-treated premise water distribution system, ~30% or lower proportion of positive Legionella samples should occur. Anything above that cutoff is either very unlikely or not expected at all and indicates a problem in the water system.

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ZHANG, WEN, TAKETOSHI YOSHIDA, and XIJIN TANG. "DISTRIBUTION OF MULTI-WORDS IN CHINESE AND ENGLISH DOCUMENTS." International Journal of Information Technology & Decision Making 08, no. 02 (June 2009): 249–65. http://dx.doi.org/10.1142/s0219622009003399.

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As a hybrid of N-gram in natural language processing and collocation in statistical linguistics, multi-word is becoming a hot topic in area of text mining and information retrieval. In this paper, a study concerning distribution of multi-words is carried out to explore a theoretical basis for probabilistic term-weighting scheme. Specifically, the Poisson distribution, zero-inflated binomial distribution, and G-distribution are comparatively studied on a task of predicting probabilities of multi-words' occurrences using these distributions, for both technical multi-words and nontechnical multi-words. In addition, a rule-based multi-word extraction algorithm is proposed to extract multi-words from texts based on words' occurring patterns and syntactical structures. Our experimental results demonstrate that G-distribution has the best capability to predict probabilities of frequency of multi-words' occurrence and the Poisson distribution is comparable to zero-inflated binomial distribution in estimation of multi-word distribution. The outcome of this study validates that burstiness is a universal phenomenon in linguistic count data, which is applicable not only for individual content words but also for multi-words.

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Martínez-Flórez, Guillermo, Víctor Leiva, Emilio Gómez-Déniz, and Carolina Marchant. "A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess." Symmetry 12, no. 9 (September 1, 2020): 1439. http://dx.doi.org/10.3390/sym12091439.

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In this paper, we consider skew-normal distributions for constructing new a distribution which allows us to model proportions and rates with zero/one inflation as an alternative to the inflated beta distributions. The new distribution is a mixture between a Bernoulli distribution for explaining the zero/one excess and a censored skew-normal distribution for the continuous variable. The maximum likelihood method is used for parameter estimation. Observed and expected Fisher information matrices are derived to conduct likelihood-based inference in this new type skew-normal distribution. Given the flexibility of the new distributions, we are able to show, in real data scenarios, the good performance of our proposal.

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Unhapipat, Suntaree, Montip Tiensuwan, and Nabendu Pal. "Bayesian Predictive Inference for Zero-Inflated Poisson (ZIP) Distribution with Applications." American Journal of Mathematical and Management Sciences 37, no. 1 (October 20, 2017): 66–79. http://dx.doi.org/10.1080/01966324.2017.1380545.

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He, H., W. Wang, J. Hu, R. Gallop, P. Crits-Christoph, and Y. Xia. "Distribution-free inference of zero-inflated binomial data for longitudinal studies." Journal of Applied Statistics 42, no. 10 (March 18, 2015): 2203–19. http://dx.doi.org/10.1080/02664763.2015.1023270.

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Schwartz, Jacob, and David E. Giles. "Bias-reduced maximum likelihood estimation of the zero-inflated Poisson distribution." Communications in Statistics - Theory and Methods 45, no. 2 (January 13, 2016): 465–78. http://dx.doi.org/10.1080/03610926.2013.824590.

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MÖller, Tobias A., Christian H. Weiß, and Hee-Young Kim. "Modelling counts with state-dependent zero inflation." Statistical Modelling 20, no. 2 (October 25, 2018): 127–47. http://dx.doi.org/10.1177/1471082x18800514.

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We introduce a state-dependent zero-inflation mechanism for count distributions with unbounded or bounded support. Instead of uniformly downweighting the parent distribution, this flexible approach allows us to generate most of the zeros from either low or high counts. We derive the stochastic properties of the inflated distributions and discuss special instances designed for zero inflation caused by, for example, excessive demand or underreporting. Furthermore, we apply the state-dependent zero-inflation mechanism to generalize existing models for count time series with bounded support.

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Cantoni, Eva, and Marie Auda. "Stochastic variable selection strategies for zero-inflated models." Statistical Modelling 18, no. 1 (June 30, 2017): 3–23. http://dx.doi.org/10.1177/1471082x17711068.

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When count data exhibit excess zero, that is more zero counts than a simpler parametric distribution can model, the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) models are often used. Variable selection for these models is even more challenging than for other regression situations because the availability of p covariates implies 4 p possible models. We adapt to zero-inflated models an approach for variable selection that avoids the screening of all possible models. This approach is based on a stochastic search through the space of all possible models, which generates a chain of interesting models. As an additional novelty, we propose three ways of extracting information from this rich chain and we compare them in two simulation studies, where we also contrast our approach with regularization (penalized) techniques available in the literature. The analysis of a typical dataset that has motivated our research is also presented, before concluding with some recommendations.

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Krieg, Sabine, Harm Jan Boonstra, and Marc Smeets. "Small-Area Estimation with Zero-Inflated Data – a Simulation Study." Journal of Official Statistics 32, no. 4 (December 1, 2016): 963–86. http://dx.doi.org/10.1515/jos-2016-0051.

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Abstract Many target variables in official statistics follow a semicontinuous distribution with a mixture of zeros and continuously distributed positive values. Such variables are called zero inflated. When reliable estimates for subpopulations with small sample sizes are required, model-based small-area estimators can be used, which improve the accuracy of the estimates by borrowing information from other subpopulations. In this article, three small-area estimators are investigated. The first estimator is the EBLUP, which can be considered the most common small-area estimator and is based on a linear mixed model that assumes normal distributions. Therefore, the EBLUP is model misspecified in the case of zero-inflated variables. The other two small-area estimators are based on a model that takes zero inflation explicitly into account. Both the Bayesian and the frequentist approach are considered. These small-area estimators are compared with each other and with design-based estimation in a simulation study with zero-inflated target variables. Both a simulation with artificial data and a simulation with real data from the Dutch Household Budget Survey are carried out. It is found that the small-area estimators improve the accuracy compared to the design-based estimator. The amount of improvement strongly depends on the properties of the population and the subpopulations of interest.

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Brodziak, Jon, and William A. Walsh. "Model selection and multimodel inference for standardizing catch rates of bycatch species: a case study of oceanic whitetip shark in the Hawaii-based longline fishery." Canadian Journal of Fisheries and Aquatic Sciences 70, no. 12 (December 2013): 1723–40. http://dx.doi.org/10.1139/cjfas-2013-0111.

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One key issue for standardizing catch per unit effort (CPUE) of bycatch species is how to model observations of zero catch per fishing operation. Typically, the fraction of zero catches is high, and catch counts may be overdispersed. In this study, we develop a model selection and multimodel inference approach to standardize CPUE in a case study of oceanic whitetip shark (Carcharhinus longimanus) bycatch in the Hawaii-based pelagic longline fishery. Alternative hypotheses for shark catch per longline set were characterized by the variance to mean ratio of the count distribution. Zero-inflated and non-inflated Poisson, negative binomial, and delta-gamma models were fit to fishery observer data using stepwise variable selection. Alternative hypotheses were compared using multimodel inference. Results from the best-fitting zero-inflated negative binomial model showed that standardized CPUE of oceanic whitetip sharks decreased by about 90% during 1995–2010 because of increased zero catch sets and decreased CPUE on sets with positive catch. Our model selection approach provides an objective way to address the question of how to treat zero catches when analyzing bycatch CPUE.

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Burger, Divan Aristo, Robert Schall, Johannes Theodorus Ferreira, and Ding‐Geng Chen. "A robust Bayesian mixed effects approach for zero inflated and highly skewed longitudinal count data emanating from the zero inflated discrete Weibull distribution." Statistics in Medicine 39, no. 9 (April 30, 2020): 1275–91. http://dx.doi.org/10.1002/sim.8475.

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Dobbie, M. J., and A. H. Welsh. "Models for zero-inflated count data using the Neyman type A distribution." Statistical Modelling 1, no. 1 (January 1, 2001): 65–80. http://dx.doi.org/10.1191/147108201128096.

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Dobbie, Melissa J., and Alan H. Welsh. "Models for zero-inflated count data using the Neyman type A distribution." Statistical Modelling: An International Journal 1, no. 1 (April 2001): 65–80. http://dx.doi.org/10.1177/1471082x0100100106.

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Kumar, C. Satheesh, and A. Riyaz. "On some aspects of a generalized alternative zero-inflated logarithmic series distribution." Communications in Statistics - Simulation and Computation 46, no. 4 (December 18, 2016): 2689–700. http://dx.doi.org/10.1080/03610918.2015.1057287.

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Fu, Ying-Zi. "Model Selection of Zero-Inflated Generalized Power Series Distribution with Missing Responses." Communications in Statistics - Theory and Methods 41, no. 6 (March 15, 2012): 1013–28. http://dx.doi.org/10.1080/03610926.2010.535633.

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Satheesh Kumar, C., and A. Riyaz. "A zero-inflated logarithmic series distribution of order k and its applications." AStA Advances in Statistical Analysis 99, no. 1 (March 26, 2014): 31–43. http://dx.doi.org/10.1007/s10182-014-0229-1.

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Zhang, Huiming, Kai Tan, and Bo Li. "COM-negative binomial distribution: modeling overdispersion and ultrahigh zero-inflated count data." Frontiers of Mathematics in China 13, no. 4 (August 2018): 967–98. http://dx.doi.org/10.1007/s11464-018-0714-z.

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Wang, Fu-Kwun, and Shalemu Sharew Hailemariam. "Sampling plans for the zero-inflated Poisson distribution in the food industry." Food Control 85 (March 2018): 359–68. http://dx.doi.org/10.1016/j.foodcont.2017.10.021.

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Chen, Tian, Pan Wu, Wan Tang, Hui Zhang, Changyong Feng, Jeanne Kowalski, and Xin M. Tu. "Variable selection for distribution-free models for longitudinal zero-inflated count responses." Statistics in Medicine 35, no. 16 (February 4, 2016): 2770–85. http://dx.doi.org/10.1002/sim.6892.

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Romanuik, Sean F., and Bonnie L. Gray. "Zero-Inflated Poisson Distribution of Sedimented Cells in Multi-Layered Microwell Arrays." Journal of The Electrochemical Society 168, no. 5 (May 1, 2021): 057510. http://dx.doi.org/10.1149/1945-7111/abf5f7.

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Rojas, Fernando, Peter Wanke, Giuliani Coluccio, Juan Vega-Vargas, and Gonzalo F. Huerta-Canepa. "Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand." PeerJ Computer Science 6 (September 14, 2020): e298. http://dx.doi.org/10.7717/peerj-cs.298.

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This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zero-inflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston’s method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.

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AFGANI, L. M. JAMALUDDIN Al. "Accuracy of Zero Inflated Generalized Poisson Exponentially Moving Average Control Chart." Jurnal Matematika, Statistika dan Komputasi 18, no. 1 (September 2, 2021): 121–29. http://dx.doi.org/10.20956/j.v18i1.14035.

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The Zero-Inflated Generalized Poisson (ZIGP) distribution is a case-based distribution where the discrete data has a large number of zeros and an overdispersion occurs, i.e. the variance is greater than the mean value. The purpose of this study is to determine the Exponential Weight Moving Average (EWMA) control chart with the assumption that the data has a Zero-Inflated Generalized Poisson (ZIP) distribution. The results show that the ARL value of the ARL ZIGP EWMA control chart has better accuracy when compared to when using the ZIP EWMA control chart on ZIGP distributed data. This is indicated by the smaller ARL value compared to the ZIP EWMA control chart, namely when φ = 1.4, and φ = 0.6. So that the ARL ZIGP EWMA control chart has a fairly good accuracy in detecting out of control conditions for ZIGP distributed data. In addition, the modified ARL shows the same values before and after the modification for the underdispersion data and shows a larger or negative value for the overdispersion data. This can eliminate or reduce errors in analyzing the accuracy of the control chart.

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Mott, P. H., C. M. Roland, and S. E. Hassan. "Strains in an Inflated Rubber Sheet." Rubber Chemistry and Technology 76, no. 2 (May 1, 2003): 326–33. http://dx.doi.org/10.5254/1.3547746.

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Abstract A technique to measure the strain distribution of an inflated membrane is described, and applied to natural rubber inflated to pole biaxial strains of 12%, 26%, and 33%. The radial strain was found to be nearly constant over the entire surface, while the circumferential strain falls to zero at the edge. Thus, biaxial deformation is limited to a narrow region in the vicinity of the pole. These results are quite different from (the very limited) data published previously. The stress-strain behavior was also measured in uniaxial tension, and the strain distribution of the inflated membrane calculated using finite elements. The experiment results and the modeling were in good agreement.

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He, B., M. Xie, T. N. Goh, and P. Ranjan. "On the Estimation Error in Zero-Inflated Poisson Model for Process Control." International Journal of Reliability, Quality and Safety Engineering 10, no. 02 (June 2003): 159–69. http://dx.doi.org/10.1142/s0218539303001068.

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The control chart based on a Poisson distribution has often been used to monitor the number of defects in sampling units. However, many false alarms could be observed due to extra zero counts, especially for high-quality processes. Therefore, some alternatives have been developed to alleviate this problem, one of which is the control chart based on the zero-inflated Poisson distribution. This distribution takes into account the extra zeros present in the data, and yield more accurate results than the Poisson distribution. However, implementing a control chart is often based on the assumption that the parameters are either known or an accurate estimate is available. For a high quality process, an accurate estimate may require a very large sample size, which is seldom available. In this paper the effect of estimation error is investigated. An analytical approximation is derived to compute shift detection probability and run length distribution. The study shows that the false alarm rates are higher than the desirable level for smaller values of the sample size. This is further supported by smaller average run length. In general, the quantitative results from this paper can be utilized to select a minimum size of the initial sample for estimating the control limits so that certain average run length requirements are met.

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Truong, Buu-Chau, Van-Buol Nguyen, Hoang-Vinh Truong, and Thi Diem-Chinh Ho. "Comparison of Optim, Nleqslv and MaxLik to Estimate Parameters in Some of Regression Models." Journal of Advanced Engineering and Computation 3, no. 4 (December 31, 2019): 532. http://dx.doi.org/10.25073/jaec.201934.262.

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Our main goal in this article is to present the approaches and examples of three functions in R consist of optim, nleqslv and maxLik function to detect the optimization solution of the estimating function in the regression models. We then compare the results with numerous sample sizes (n=150, 300 and 500), the execution time of R code, as well as Normal Q - Q plots of three approaches through some of regression models such as the zero-inflated Binomial (ZIB) regression model, logistic regression model, the zero-inflated Poisson (ZIP) regression model and the zero-inflated Bernoulli (ZIBer) regression model. Finally, we discuss potential research directions in the coming times. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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Dai, Lin, Michael D. Sweat, and Mulugeta Gebregziabher. "Modeling excess zeros and heterogeneity in count data from a complex survey design with application to the demographic health survey in sub-Saharan Africa." Statistical Methods in Medical Research 27, no. 1 (July 20, 2016): 208–20. http://dx.doi.org/10.1177/0962280215626608.

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Purpose To show a novel application of a weighted zero-inflated negative binomial model in modeling count data with excess zeros and heterogeneity to quantify the regional variation in HIV-AIDS prevalence in sub-Saharan African countries. Methods Data come from latest round of the Demographic and Health Survey (DHS) conducted in three countries (Ethiopia-2011, Kenya-2009 and Rwanda-2010) using a two-stage cluster sampling design. The outcome is an aggregate count of HIV cases in each census enumeration area of each country. The outcome data are characterized by excess zeros and heterogeneity due to clustering. We compare scale weighted zero-inflated negative binomial models with and without random effects to account for zero-inflation, complex survey design and clustering. Finally, we provide marginalized rate ratio estimates from the best zero-inflated negative binomial model. Results The best fitting zero-inflated negative binomial model is scale weighted and with a common random intercept for the three countries. Rate ratio estimates from the final model show that HIV prevalence is associated with age and gender distribution, HIV acceptance, HIV knowledge, and its regional variation is associated with divorce rate, burden of sexually transmitted diseases and rural residence. Conclusions Scale weighted zero-inflated negative binomial with proper modeling of random effects is shown to be the best model for count data from a complex survey design characterized by excess zeros and extra heterogeneity. In our data example, the final rate ratio estimates show significant regional variation in the factors associated with HIV prevalence indicating that HIV intervention strategies should be tailored to the unique factors found in each country.

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