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Journal articles on the topic 'Zero-inflated negative binomial regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Astuti, Cindy Cahyaning, and Angga Dwi Mulyanto. "Estimation Parameters And Modelling Zero Inflated Negative Binomial." CAUCHY 4, no. 3 (November 30, 2016): 115. http://dx.doi.org/10.18860/ca.v4i3.3656.

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Regression analysis is used to determine relationship between one or several response variable (Y) with one or several predictor variables (X). Regression model between predictor variables and the Poisson distributed response variable is called Poisson Regression Model. Since, Poisson Regression requires an equality between mean and variance, it is not appropriate to apply this model on overdispersion (variance is higher than mean). Poisson regression model is commonly used to analyze the count data. On the count data type, it is often to encounteredd some observations that have zero value with large proportion of zero value on the response variable (zero Inflation). Poisson regression can be used to analyze count data but it has not been able to solve problem of excess zero value on the response variable. An alternative model which is more suitable for overdispersion data and can solve the problem of excess zero value on the response variable is Zero Inflated Negative Binomial (ZINB). In this research, ZINB is applied on the case of Tetanus Neonatorum in East Java. The aim of this research is to examine the likelihood function and to form an algorithm to estimate the parameter of ZINB and also applying ZINB model in the case of Tetanus Neonatorum in East Java. Maximum Likelihood Estimation (MLE) method is used to estimate the parameter on ZINB and the likelihood function is maximized using Expectation Maximization (EM) algorithm. Test results of ZINB regression model showed that the predictor variable have a partial significant effect at negative binomial model is the percentage of pregnant women visits and the percentage of maternal health personnel assisted, while the predictor variables that have a partial significant effect at zero inflation model is the percentage of neonatus visits.

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2

Faroughi, Pouya, and Noriszura Ismail. "Bivariate zero-inflated negative binomial regression model with applications." Journal of Statistical Computation and Simulation 87, no. 3 (July 28, 2016): 457–77. http://dx.doi.org/10.1080/00949655.2016.1213843.

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3

Sin, Hye-Yeon, and Joonsung Kang. "Comparison of penalized zero inflated negative binomial regression methods." Journal of the Korean Data And Information Science Society 32, no. 4 (July 31, 2021): 715–37. http://dx.doi.org/10.7465/jkdi.2021.32.4.715.

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4

Rumahorbo, Kusni Rohani, Budi Susetyo, and Kusman Sadik. "PEMODELAN DATA TERSENSOR KANAN MENGGUNAKAN ZERO INFLATED NEGATIVE BINOMIAL DAN HURDLE NEGATIVE BINOMIAL." Indonesian Journal of Statistics and Its Applications 3, no. 2 (June 30, 2019): 184–201. http://dx.doi.org/10.29244/ijsa.v3i2.247.

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Health is a very important thing for humanity. One way to look at a person's health condition is through the number of unhealthy days which can also shows the productivity of the community in a region. Modeling the number of unhealthy days which are examples of count data can be done using Poisson regression. Problems that are often faced in data counts are overdispersion and excess zero. Poisson regression cannot be applied to data that experiences both of these. Zero Inflated Negative Binomial and Hurdle Negative Binomial modeling was performed on data with 2 conditions, uncensored and censored. The explanatory variables used are gender, age, marital status, education level, home ownership status and rural-urban status. According to the results of the AIC and RMSE calculation, Zero Inflated Negative Binomial on censored data showed the best performance for estimating the number of unhealthy days.

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5

Kim, Dong-Seok, Seul-Gi Jeong, and Dong-Hee Lee. "Bivariate Zero-Inflated Negative Binomial Regression Model with Heterogeneous Dispersions." Communications for Statistical Applications and Methods 18, no. 5 (September 30, 2011): 571–79. http://dx.doi.org/10.5351/ckss.2011.18.5.571.

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6

Purnama, Drajat Indra. "Comparison of Zero Inflated Poisson (ZIP) Regression, Zero Inflated Negative Binomial Regression (ZINB) and Binomial Negative Hurdle Regression (HNB) to Model Daily Cigarette Consumption Data for Adult Population in Indonesia." Jurnal Matematika, Statistika dan Komputasi 17, no. 3 (May 12, 2021): 357–69. http://dx.doi.org/10.20956/j.v17i3.12278.

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Smoking is a habit that is not good for health. Smoking habits are generally practiced by adults but it is possible for teenagers to do so.The Report of Southeast Asia Tobacco Control Alliance (SEATCA) entitled The Tobacco Control Atlas, ASEAN Region shows that Indonesia is the country with the highest number of smokers in ASEAN, namely 65.19 million people. This figure is equivalent to 34 percent of the total population of Indonesia in 2016. Based on these data, the authors are interested in modeling the daily cigarette consumption data for adults in Indonesia obtained from the 2015 Indonesia Family Life Survey. The variables used include the variable amount of cigarette consumption, education, level of welfare and income per month. The author wants to compare the best model that can be used to model the daily cigarette consumption of adults in Indonesia. The models being compared are Zero Inflated Poisson Regression (ZIP), Zero Inflated Negative Binomial Regression (ZINB) and Binomial Negative Hurdle Regression (HNB). The comparison results of the three models obtained that the best model is the Zero Inflated Negative Binomial (ZINB) Regression model because it has the smallest Akaike's Information Criterion (AIC) value.

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7

Amaliana, Luthfatul, Umu Sa’adah, and Ni Wayan Surya Wardhani. "Modeling Tetanus Neonatorum case using the regression of negative binomial and zero-inflated negative binomial." Journal of Physics: Conference Series 943 (December 2017): 012051. http://dx.doi.org/10.1088/1742-6596/943/1/012051.

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8

Neelon, Brian. "Bayesian Zero-Inflated Negative Binomial Regression Based on Pólya-Gamma Mixtures." Bayesian Analysis 14, no. 3 (September 2019): 829–55. http://dx.doi.org/10.1214/18-ba1132.

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9

Wang, Peiming, and Joseph D. Alba. "A zero-inflated negative binomial regression model with hidden Markov chain." Economics Letters 92, no. 2 (August 2006): 209–13. http://dx.doi.org/10.1016/j.econlet.2006.02.009.

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10

Preisser, John S., Kalyan Das, D. Leann Long, and Kimon Divaris. "Marginalized zero-inflated negative binomial regression with application to dental caries." Statistics in Medicine 35, no. 10 (November 15, 2015): 1722–35. http://dx.doi.org/10.1002/sim.6804.

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11

Ridout, Martin, John Hinde, and Clarice G. B. Demétrio. "A Score Test for Testing a Zero‐Inflated Poisson Regression Model Against Zero‐Inflated Negative Binomial Alternatives." Biometrics 57, no. 1 (March 2001): 219–23. http://dx.doi.org/10.1111/j.0006-341x.2001.00219.x.

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12

Yusuf, Oyindamola B., Rotimi Felix Afolabi, and Ayoola S. Ayoola. "Modelling Excess Zeros in Count Data with Application to Antenatal Care Utilisation." International Journal of Statistics and Probability 7, no. 3 (April 17, 2018): 22. http://dx.doi.org/10.5539/ijsp.v7n3p22.

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Poisson and negative binomial regression models have been used as a standard for modelling count outcomes; but these methods do not take into account the problems associated with excess zeros. However, zero-inflated and hurdle models have been proposed to model count data with excess zeros. The study therefore compared the performance of Zero-inflated (Zero-inflated Poisson (ZIP) and Zero-inflated negative binomial (ZINB)), and hurdle (Hurdle Poisson (HP) and Hurdle negative binomial (HNB)) models in determining the factors associated with the number of Antenatal Care (ANC) visits in Nigeria. Using the 2013 Nigeria Demographic and Health Survey dataset, a sample of 19 652 women of reproductive age who gave birth five years prior to the survey and provided information about ANC visits was utilised. Data were analysed using descriptive statistics, ZIP, ZINB, HP and HNB models, and information criteria (AIC/BIC) was used to assess model fit. Participants’ mean age was 29.5 ± 7.3 years and median number of ANC visits was 4 (range: 0 - 30). About half (54.9%) of the participants had at least 4 ANC visits while 33.9% had none. The ZINB (AIC = 83 039.4; BIC = 83 470.3) fitted the data better than the ZIP or HP; however, HNB (AIC = 83 041.4; BIC = 83 472.3) competed favorably well with it. The Zero-inflated negative binomial model provided the better fit for the data. We suggest the Zero-inflated negative binomial model for count data with excess zeros of unknown sources such as the number of ANC visits in Nigeria.

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13

SEKARMINI, NI MADE, I. KOMANG GDE SUKARSA, and I. GUSTI AYU MADE SRINADI. "PENERAPAN REGRESI ZERO-INFLATED NEGATIVE BINOMIAL (ZINB) UNTUK PENDUGAAN KEMATIAN ANAK BALITA." E-Jurnal Matematika 2, no. 4 (November 29, 2013): 11. http://dx.doi.org/10.24843/mtk.2013.v02.i04.p052.

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One method of regression analysis used to analyze the count data is Poisson regression. Poisson regression requires that the mean value equal to the value of variance (equidispersion). However, sometimes the data is going overdispersion the state variance values ??greater than the mean value. One of the causes overdispersion is the excessive number of zero values ??on the response variable (excess zeros). One method of analysis that can be used on data that had overdispersion due to excess zeros is regression Zero-Inflated Negative Binomial (ZINB). The data that can be analyzed using the ZINB regression is the early childhood mortality in the province of Bali because much of the data is zero. The analysis showed that the data had overdispersion on Poisson regression, so the ZINB regression analysis was used. From the results of the ZINB regression can overcome overdispersion so it was better than the Poisson Regression Model.

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14

Weng, Jinxian, Ying En Ge, and Hao Han. "Evaluation of Shipping Accident Casualties using Zero-inflated Negative Binomial Regression Technique." Journal of Navigation 69, no. 2 (October 29, 2015): 433–48. http://dx.doi.org/10.1017/s0373463315000788.

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This study develops a Zero-Inflated Negative Binomial (ZINB) regression model to evaluate the factors influencing the loss of human life in shipping accidents using ten years' ship accident data in the South China Sea. The ZINB regression model results show that the expected loss of human life is higher for collision, fire/explosion, contact, grounding, hull damage, machinery damage/failure and capsizing accidents occurring in adverse weather conditions during night periods. Sinking can cause the highest loss of life compared to all other accident types. There are fewer fatalities and missing people when the ship involved in an accident is moored or docked. The results also reveal that the loss of human life is associated with shipping accidents occurring far away from the coastal area/harbour/ports. The results of this study are beneficial for policy-makers in proposing efficient strategies to reduce shipping accident casualties in the South China Sea.

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15

So, Sunha, Dong-Hee Lee, and Byoung Cheol Jung. "An alternative bivariate zero-inflated negative binomial regression model using a copula." Economics Letters 113, no. 2 (November 2011): 183–85. http://dx.doi.org/10.1016/j.econlet.2011.07.017.

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16

Garay, Aldo M., Elizabeth M. Hashimoto, Edwin M. M. Ortega, and Víctor H. Lachos. "On estimation and influence diagnostics for zero-inflated negative binomial regression models." Computational Statistics & Data Analysis 55, no. 3 (March 2011): 1304–18. http://dx.doi.org/10.1016/j.csda.2010.09.019.

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17

Minami, M., C. E. Lennert-Cody, W. Gao, and M. Román-Verdesoto. "Modeling shark bycatch: The zero-inflated negative binomial regression model with smoothing." Fisheries Research 84, no. 2 (April 2007): 210–21. http://dx.doi.org/10.1016/j.fishres.2006.10.019.

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18

Fisher, William H., Stephanie W. Hartwell, and Xiaogang Deng. "Managing Inflation." Crime & Delinquency 63, no. 1 (December 9, 2016): 77–87. http://dx.doi.org/10.1177/0011128716679796.

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Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

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19

Preisser, John S., D. Leann Long, and John W. Stamm. "Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts." Caries Research 51, no. 3 (2017): 198–208. http://dx.doi.org/10.1159/000452675.

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Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two data sets, one consisting of fictional dmft counts in 2 groups and the other on DMFS among schoolchildren from a randomized clinical trial comparing 3 toothpaste formulations to prevent incident dental caries, are analyzed with negative binomial hurdle, zero-inflated negative binomial, and marginalized zero-inflated negative binomial models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the randomized clinical trial were similar despite their distinctive interpretations. The choice of statistical model class should match the study's purpose, while accounting for the broad decline in children's caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts.

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20

I T Utami. "Pemodelan Zero Inflated Negative Binomial (ZINB) Pada Kasus Jumlah Bepergian Penduduk Provinsi Sulawesi Tengah." JURNAL ILMIAH MATEMATIKA DAN TERAPAN 17, no. 2 (November 19, 2020): 202–11. http://dx.doi.org/10.22487/2540766x.2020.v17.i2.15302.

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Traveling is something that has been done by every resident, especially the people of Central Sulawesi province. The people of Central Sulawesi province who travel have a certain purpose either because of their own desires or following others. The number of people traveling from Central Sulawesi Province can be analyzed using the zero inflated negative binomial regression method (ZINB). ZINB regression is a method used to model calculated or discrete data with many zero values on the response variable (zero inflation) and overdispersion occurs. The result shows that the factors affecting the number of people traveling population from the Central Sulawesi Province are age (X1), the people who have jobs in mining and quarrying (X32), the people who have jobs in electricity and gas (X34). The zero inflated negative binomial (ZINB) regression model is better at modeling cases of the number of people traveling in Central Sulawesi Province in 2016 compared to the Poisson regression model because it has the smallest AIC value.

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21

Inan, Gul, John Preisser, and Kalyan Das. "A Score Test for Testing a Marginalized Zero-Inflated Poisson Regression Model Against a Marginalized Zero-Inflated Negative Binomial Regression Model." Journal of Agricultural, Biological and Environmental Statistics 23, no. 1 (November 22, 2017): 113–28. http://dx.doi.org/10.1007/s13253-017-0314-5.

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22

Kim, Jong-Min, and Sunghae Jun. "Zero-Inflated Poisson and Negative Binomial Regressions for Technology Analysis." International Journal of Software Engineering and Its Applications 10, no. 12 (December 31, 2016): 431–48. http://dx.doi.org/10.14257/ijseia.2016.10.12.36.

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23

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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Kim, Dongseok. "A simple zero inflated bivariate negative binomial regression model with different dispersion parameters." Journal of the Korean Data and Information Science Society 24, no. 4 (July 31, 2013): 895–900. http://dx.doi.org/10.7465/jkdi.2013.24.4.895.

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25

Rehder, Kristoffer, and Sarah Bowen. "PTSD Symptom Severity, Cannabis, and Gender: A Zero-Inflated Negative Binomial Regression Model." Substance Use & Misuse 54, no. 8 (February 15, 2019): 1309–18. http://dx.doi.org/10.1080/10826084.2019.1575421.

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26

Chai, Tian, De-qi Xiong, and Jinxian Weng. "A Zero-Inflated Negative Binomial Regression Model to Evaluate Ship Sinking Accident Mortalities." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 11 (July 31, 2018): 65–72. http://dx.doi.org/10.1177/0361198118787388.

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Sinking accidents are a seafarer’s nightmare. Using 10 years’ of worldwide sinking accident data, this study aims to develop a mortality count model to evaluate the human life loss resulting from sinking accidents using zero-inflated negative binomial regression approaches. The model results show that the increase of the expected human life loss is the largest when a ship suffers a precedent accident of capsizing, followed by fire/explosion or collisions. Lower human life loss is associated with contact and machinery/hull damage accidents. Consistent with our expectation, cruise ships involved in sinking accidents usually suffer more human life loss than non-cruise ships and there is be a bigger mortality count for sinking accidents that occur far away from the coastal area/harbor/port. Fatalities can be less when the ship is moored or docked. The results of this study are beneficial for policy-makers in proposing efficient strategies to reduce sinking accident mortalities.

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27

Liu, Chenhui, Mo Zhao, Wei Li, and Anuj Sharma. "Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes." Analytic Methods in Accident Research 17 (March 2018): 32–46. http://dx.doi.org/10.1016/j.amar.2018.03.001.

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28

Abiodun, Gbenga J., Olusola S. Makinde, Abiodun M. Adeola, Kevin Y. Njabo, Peter J. Witbooi, Ramses Djidjou-Demasse, and Joel O. Botai. "A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa." International Journal of Environmental Research and Public Health 16, no. 11 (June 5, 2019): 2000. http://dx.doi.org/10.3390/ijerph16112000.

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Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box–Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box–Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box–Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe―two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.

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29

Li, Rongxia, Aaron R. Weiskittel, and John A. Kershaw. "Modeling annualized occurrence, frequency, and composition of ingrowth using mixed-effects zero-inflated models and permanent plots in the Acadian Forest Region of North America." Canadian Journal of Forest Research 41, no. 10 (October 2011): 2077–89. http://dx.doi.org/10.1139/x11-117.

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Forest tree ingrowth is a highly variable and largely stochastic process. Consequently, predicting occurrence, frequency, and composition of ingrowth is a challenging task but of great importance in long-term forest growth and yield model projections. However, ingrowth data often require different statistical techniques other than traditional Gaussian regression, because these data are often bounded, skewed, and non-normal and commonly contain a large fraction of zeros. This study presents a set of regression models based on discrete Poisson and negative binomial probability distributions for ingrowth data collected from permanent sample plots in the Acadian Forest Region of North America. Models considered here include regular Poisson, zero-inflated Poisson (ZIP), zero-altered Poisson (ZAP; hurdle Poisson), regular negative binomial (NB), zero-inflated negative binomial (ZINB), and zero-altered negative binomial (ZANB; hurdle NB). Plot-level random effects were incorporated into each of these models. The ZINB model with random effects was found to provide the best fit statistics for modeling annualized occurrence and frequency of ingrowth. The key explanatory variables were stand basal area per hectare, percentage of hardwood basal area, number of trees per hectare, a measure of site quality, and the minimum measured diameter at breast height of each plot. A similar model was developed to predict species composition. All models showed logical behavior despite the high variability observed in the original data.

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Wang, Peiming. "A bivariate zero-inflated negative binomial regression model for count data with excess zeros." Economics Letters 78, no. 3 (March 2003): 373–78. http://dx.doi.org/10.1016/s0165-1765(02)00262-8.

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Garay, Aldo M., Victor H. Lachos, and Heleno Bolfarine. "Bayesian estimation and case influence diagnostics for the zero-inflated negative binomial regression model." Journal of Applied Statistics 42, no. 6 (January 6, 2015): 1148–65. http://dx.doi.org/10.1080/02664763.2014.995610.

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Alam, Morshed, Naim Al Mahi, and Munni Begum. "Zero-Inflated Models for RNA-Seq Count Data." Journal of Biomedical Analytics 1, no. 2 (September 21, 2018): 55–70. http://dx.doi.org/10.30577/jba.2018.v1n2.23.

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One of the main objectives of many biological studies is to explore differential gene expression profiles between samples. Genes are referred to as differentially expressed (DE) if the read counts change across treatments or conditions systematically. Poisson and negative binomial (NB) regressions are widely used methods for non-over-dispersed (NOD) and over-dispersed (OD) count data respectively. However, in the presence of excessive number of zeros, these methods need adjustments. In this paper, we consider a zero-inflated Poisson mixed effects model (ZIPMM) and zero-inflated negative binomial mixed effects model (ZINBMM) to address excessive zero counts in the NOD and OD RNA-seq data respectively in the presence of random effects. We apply these methods to both simulated and real RNA-seq datasets. The ZIPMM and ZINBMM perform better on both simulated and real datasets.

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33

DEWANTI, NI PUTU PREMA, MADE SUSILAWATI, and I. GUSTI AYU MADE SRINADI. "PERBANDINGAN REGRESI ZERO INFLATED POISSON (ZIP) DAN REGRESI ZERO INFLATED NEGATIVE BINOMIAL (ZINB) PADA DATA OVERDISPERSION (Studi Kasus: Angka Kematian Ibu di Provinsi Bali)." E-Jurnal Matematika 5, no. 4 (November 30, 2016): 133. http://dx.doi.org/10.24843/mtk.2016.v05.i04.p132.

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Poisson regression is a nonlinear regression which is often used for count data and has equidispersion assumption (variance value equal to mean value). However in practice, equidispersion assumption is often violated. One of it violations is overdispersion (variance value greater than the mean value). One of the causes of overdipersion is excessive number of zero values on the response variable (excess zeros). There are many methods to handle overdispersion because of excess zeros. Two of them are Zero Inflated Poisson (ZIP) regression and Zero Inflated Negative Binomial (ZINB) regression. The purpose of this research is to determine which regression models is better in handling overdispersion data. The data that can be analyzed using the ZIP and ZINB regression is maternal mortality rate in the Province of Bali. Maternal mortality rate data has proportion of zeros value more than 50% on the response variable. In this research, ZINB regression better than ZIP regression for modeling maternal mortality rate. The independent variable that affects the number of maternal mortality rate in the Province of Bali is the percentage of mothers who carry a pregnancy visit, with ZINB regression models and .

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Pudney, Stephen. "intcount: A command for fitting count-data models from interval data." Stata Journal: Promoting communications on statistics and Stata 19, no. 3 (September 2019): 645–66. http://dx.doi.org/10.1177/1536867x19874240.

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In this article, I describe a community-contributed command, intcount, that fits one of several regression models for count data observed in interval form. The models available are Poisson, negative binomial, and binomial, and they can be fit in standard or zero-inflated form. I illustrate the command with an application to analysis of data from the UK Understanding Society survey on the demand for healthcare services.

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Park, Man Sik, Jin Ki Eom, Jungsoon Choi, and Tae-Young Heo. "Analysis of the Railway Accident-Related Damages in South Korea." Applied Sciences 10, no. 24 (December 8, 2020): 8769. http://dx.doi.org/10.3390/app10248769.

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Railway accidents are critical issues characterized by a large number of injuries and fatalities per accident due to massive public transport systems. This study proposes a new approach for evaluating the damages resulting from railway accidents using the two-part models (TPMs) such as the zero-inflated Poisson regression model (ZIP model) and the zero-inflated negative-binomial regression model (ZINB model) for the non-negative count measurements and the zero-inflated gamma regression model (ZIG model) and the zero-inflated log-normal regression model (ZILN model) for the semi-continuous measurements. The models are employed for the evaluation of the railway accidents on Korea Railroad, considering the accident damages, such as the train delay time, the number of trains delayed and the cost of considering the accident count responses, for the period 2008 to 2016. From the results obtained, we found that the human-related factors, the high-speed railway system or the Korea Train Express (KTX) and the number of casualties, are the main cost-escalating factors. The number of trains delayed and the amount of delay time tend to increase both the probability of incurring costs and the amount of cost. For better evaluation, the railway accident data should contain accurate information with less recurrence of zeros.

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Yau, Kelvin K. W., Kui Wang, and Andy H. Lee. "Zero-Inflated Negative Binomial Mixed Regression Modeling of Over-Dispersed Count Data with Extra Zeros." Biometrical Journal 45, no. 4 (June 2003): 437–52. http://dx.doi.org/10.1002/bimj.200390024.

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Moghimbeigi, Abbas, Mohammed Reza Eshraghian, Kazem Mohammad, and Brian Mcardle. "Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros." Journal of Applied Statistics 35, no. 10 (August 15, 2008): 1193–202. http://dx.doi.org/10.1080/02664760802273203.

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Kong, Maiying, Sheng Xu, Steven M. Levy, and Somnath Datta. "GEE type inference for clustered zero-inflated negative binomial regression with application to dental caries." Computational Statistics & Data Analysis 85 (May 2015): 54–66. http://dx.doi.org/10.1016/j.csda.2014.11.014.

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Montshiwa, Volition Tlhalitshi, and Ntebogang Dinah Moroke. "The Effect of Sample Size on the Efficiency of Count Data Models: Application to Marriage Data." Journal of Economics and Behavioral Studies 9, no. 3(J) (July 20, 2017): 6–18. http://dx.doi.org/10.22610/jebs.v9i3(j).1742.

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Abstract: Sample size requirements are common in many multivariate analysis techniques as one of the measures taken to ensure the robustness of such techniques, such requirements have not been of interest in the area of count data models. As such, this study investigated the effect of sample size on the efficiency of six commonly used count data models namely: Poisson regression model (PRM), Negative binomial regression model (NBRM), Zero-inflated Poisson (ZIP), Zero-inflated negative binomial (ZINB), Poisson Hurdle model (PHM) and Negative binomial hurdle model (NBHM). The data used in this study were sourced from Data First and were collected by Statistics South Africa through the Marriage and Divorce database. PRM, NBRM, ZIP, ZINB, PHM and NBHM were applied to ten randomly selected samples ranging from 4392 to 43916 and differing by 10% in size. The six models were compared using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Vuongâ€™s test for over-dispersion, McFadden RSQ, Mean Square Error (MSE) and Mean Absolute Deviation (MAD).The results revealed that generally, the Negative Binomial-based models outperformed Poisson-based models. However, the results did not reveal the effect of sample size variations on the efficiency of the models since there was no consistency in the change in AIC, BIC, Vuongâ€™s test for over-dispersion, McFadden RSQ, MSE and MAD as the sample size increased.

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Montshiwa, Volition Tlhalitshi, and Ntebogang Dinah Moroke. "The Effect of Sample Size on the Efficiency of Count Data Models: Application to Marriage Data." Journal of Economics and Behavioral Studies 9, no. 3 (July 20, 2017): 6. http://dx.doi.org/10.22610/jebs.v9i3.1742.

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Abstract: Sample size requirements are common in many multivariate analysis techniques as one of the measures taken to ensure the robustness of such techniques, such requirements have not been of interest in the area of count data models. As such, this study investigated the effect of sample size on the efficiency of six commonly used count data models namely: Poisson regression model (PRM), Negative binomial regression model (NBRM), Zero-inflated Poisson (ZIP), Zero-inflated negative binomial (ZINB), Poisson Hurdle model (PHM) and Negative binomial hurdle model (NBHM). The data used in this study were sourced from Data First and were collected by Statistics South Africa through the Marriage and Divorce database. PRM, NBRM, ZIP, ZINB, PHM and NBHM were applied to ten randomly selected samples ranging from 4392 to 43916 and differing by 10% in size. The six models were compared using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Vuong’s test for over-dispersion, McFadden RSQ, Mean Square Error (MSE) and Mean Absolute Deviation (MAD).The results revealed that generally, the Negative Binomial-based models outperformed Poisson-based models. However, the results did not reveal the effect of sample size variations on the efficiency of the models since there was no consistency in the change in AIC, BIC, Vuong’s test for over-dispersion, McFadden RSQ, MSE and MAD as the sample size increased.

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Aslam, Muhammad, Maryam Sadiq, and Tahir Mehmood. "Assessment of maternal health services utilization in Pakistan: the role of socio-demographic characteristics." Asian Biomedicine 14, no. 1 (July 13, 2020): 3–7. http://dx.doi.org/10.1515/abm-2020-0002.

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AbstractBackgroundHigh-quality prenatal care has a significant positive impact on maternal and infant health as it helps timely diagnosis and treatment of pregnancy complications.ObjectiveTo examine factors associated with the utilization of maternal health care using the optimal count regression model.MethodsA sample of 16,314 women of reproductive ages (15–49) was used. Andersen and Newman's behavioral model of health services utilization was employed for the selection of covariates. Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial (ZINB), Poisson hurdle, and negative binomial hurdle models were fitted and compared to identify the best model. Maternal health care utilization is found associated with maternal age and education, area of residence, domestic violence, the income level of family, access to media, knowledge about AIDS, parity, birth order, and having a child who later died.ResultsZINB model is found to be best fitted for the observed data resulting strong influence of mother's education and income level of the family on maternal health care utilization.ConclusionInterventions to improve maternal care services utilization should address individuals and systems to reduce social and economic marginalization.

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Eskelson, Bianca N. I., Hailemariam Temesgen, and Tara M. Barrett. "Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods." Canadian Journal of Forest Research 39, no. 9 (September 2009): 1749–65. http://dx.doi.org/10.1139/x09-086.

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Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods. The models were developed and fit to data collected by the Forest Inventory and Analysis program of the US Forest Service in Washington, Oregon, and California. For predicting cavity tree and snag abundance per stand, all three NB regression models performed better in terms of mean square prediction error than the NN imputation methods. The most similar neighbor imputation, however, outperformed the NB regression models in predicting overall cavity tree and snag abundance.

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FANG, R., B. D. WAGNER, J. K. HARRIS, and S. A. FILLON. "Zero-inflated negative binomial mixed model: an application to two microbial organisms important in oesophagitis." Epidemiology and Infection 144, no. 11 (April 6, 2016): 2447–55. http://dx.doi.org/10.1017/s0950268816000662.

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SUMMARYAltered microbial communities are thought to play an important role in eosinophilic oesophagitis, an allergic inflammatory condition of the oesophagus. Identification of the majority of organisms present in human-associated microbial communities is feasible with the advent of high throughput sequencing technology. However, these data consist of non-negative, highly skewed sequence counts with a large proportion of zeros. In addition, hierarchical study designs are often performed with repeated measurements or multiple samples collected from the same subject, thus requiring approaches to account for within-subject variation, yet only a small number of microbiota studies have applied hierarchical regression models. In this paper, we describe and illustrate the use of a hierarchical regression-based approach to evaluate multiple factors for a small number of organisms individually. More specifically, the zero-inflated negative binomial mixed model with random effects in both the count and zero-inflated parts is applied to evaluate associations with disease state while adjusting for potential confounders for two organisms of interest from a study of human microbiota sequence data in oesophagitis.

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Karaa, Imen, and Habib Chabchoub. "Zero-inflated and over-dispersed data models: Empirical evidence from insurance claim frequencies." Assurances et gestion des risques 84, no. 3-4 (February 19, 2018): 103–28. http://dx.doi.org/10.7202/1043358ar.

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The main objective of this paper is to model automobile claim frequency by using standard count regression and zero-inflated regression models. The use of the latter model is mainly motivated by its ability to handle the over dispersion and zero-inflation phenomenon. The sample data consist of claims data obtained from one randomly selected automobile insurance company in Tunisia for a single year, 2009, containing beginning drivers and drivers who have had a license for less than three years. Our estimation results show that many exogenous variables can explain the frequency of claims; they are not taken into account in calculating the basic insurance premium. Moreover, the ZI binomial negative regression outperforms the standard count models and the ZI Poisson model in handling zero-inflated and additional over dispersed claim count data.

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Seidel, E. J., J. B. Pazini, V. L. D. Tomazella, A. M. C. Vieira, F. F. Silva, J. F. S. Martins, and J. A. F. Barrigossi. "Predicting Rice Stem Stink Bug Population Dynamics Based on GAMLSS Models." Environmental Entomology 49, no. 5 (September 19, 2020): 1145–54. http://dx.doi.org/10.1093/ee/nvaa091.

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Abstract The rice stem stink bug, Tibraca limbativentris Stål (Hemiptera: Pentatomidae), is one of the most harmful insects for Brazilian rice fields. Aiming to define the most appropriate time and place for pest management measures in commercial paddy fields, we adjusted regression models (Poisson, Zero Inflated Poisson, reparametrized Zero Inflated Poisson, Negative Binomial and Zero Inflated Negative Binomial) for modeling the population variation of T. limbativentris along the phenological cycle of the flooded rice cultivation. We hypothesize that the rice stem stink bug population’s size is influenced by the rice cycle (time) and geographical positions within the crop. It was possible to predict the occurrence of the rice stem stink bug in the commercial flooded rice crop. The population of the rice stem stink bug increased significantly with the time or phenological evolution of rice. Our results indicated that the start of T. limbativentris monitoring should occur up to 45 d After Plant Emergence (DAE), from the regions along the edges of the rice paddies, which are the points of entry and higher concentration of the insect. In addition, 45 and 60 DAE were considered the crucial times for T. limbativentris control decision making in flooded rice paddies.

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Purnama, D. I. "Model Regresi Hurdle Negative Binomial (HNB) untuk Pemodelan Konsumsi Rokok di Provinsi Sulawesi Tengah." JURNAL ILMIAH MATEMATIKA DAN TERAPAN 18, no. 1 (June 14, 2021): 21–31. http://dx.doi.org/10.22487/2540766x.2021.v18.i1.15506.

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The average expenditure on cigarettes per capita in Sulawesi Tengah Province has increased in 2020. There are several factors that can affect a person's cigarette consumption including gender, age, education and health. To model cigarette consumption with several influencing factors can be use the poison regression model or the Zero Inflated Poisson (ZIP) model. However, the two regression models cannot solve the excess zero and overdispersion problems so use the Hurdle Negative Binomial (HNB) regression model. The results of the analysis of cigarette consumption data in Central Sulawesi Province using the HNB model provide the best modeling results compared to the poisson regression model and the ZIP model because it has the smallest Akaike's Information Criterion (AIC) value. The results of testing the factors that significantly influence cigarette consumption in Central Sulawesi Province in the HNB regression model, namely the count model are gender, age and health. Whereas in the zerohurdle model, it is gender, age and education.

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Pittman, Brian, Eugenia Buta, Suchitra Krishnan-Sarin, Stephanie S. O’Malley, Thomas Liss, and Ralitza Gueorguieva. "Models for Analyzing Zero-Inflated and Overdispersed Count Data: An Application to Cigarette and Marijuana Use." Nicotine & Tobacco Research 22, no. 8 (April 18, 2018): 1390–98. http://dx.doi.org/10.1093/ntr/nty072.

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Abstract Introduction This article describes different methods for analyzing counts and illustrates their use on cigarette and marijuana smoking data. Methods The Poisson, zero-inflated Poisson (ZIP), hurdle Poisson (HUP), negative binomial (NB), zero-inflated negative binomial (ZINB), and hurdle negative binomial (HUNB) regression models are considered. The different approaches are evaluated in terms of the ability to take into account zero-inflation (extra zeroes) and overdispersion (variance larger than expected) in count outcomes, with emphasis placed on model fit, interpretation, and choosing an appropriate model given the nature of the data. The illustrative data example focuses on cigarette and marijuana smoking reports from a study on smoking habits among youth e-cigarette users with gender, age, and e-cigarette use included as predictors. Results Of the 69 subjects available for analysis, 36% and 64% reported smoking no cigarettes and no marijuana, respectively, suggesting both outcomes might be zero-inflated. Both outcomes were also overdispersed with large positive skew. The ZINB and HUNB models fit the cigarette counts best. According to goodness-of-fit statistics, the NB, HUNB, and ZINB models fit the marijuana data well, but the ZINB provided better interpretation. Conclusion In the absence of zero-inflation, the NB model fits smoking data well, which is typically overdispersed. In the presence of zero-inflation, the ZINB or HUNB model is recommended to account for additional heterogeneity. In addition to model fit and interpretability, choosing between a zero-inflated or hurdle model should ultimately depend on the assumptions regarding the zeros, study design, and the research question being asked. Implications Count outcomes are frequent in tobacco research and often have many zeros and exhibit large variance and skew. Analyzing such data based on methods requiring a normally distributed outcome are inappropriate and will likely produce spurious results. This study compares and contrasts appropriate methods for analyzing count data, specifically those with an over-abundance of zeros, and illustrates their use on cigarette and marijuana smoking data. Recommendations are provided.

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Villa, G., J. Cuervo, and P. Rebollo. "PCN6 MODELING LUNG AND BREAST CANCER INCIDENCE IN SPAIN: A ZERO INFLATED NEGATIVE BINOMIAL REGRESSION APPROACH." Value in Health 11, no. 6 (November 2008): A461. http://dx.doi.org/10.1016/s1098-3015(10)66544-8.

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Moghimbeigi, Abbas. "Two-part zero-inflated negative binomial regression model for quantitative trait loci mapping with count trait." Journal of Theoretical Biology 372 (May 2015): 74–80. http://dx.doi.org/10.1016/j.jtbi.2015.02.016.

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Weng, Jinxian, Dong Yang, Ting Qian, and Zhi Huang. "Combining zero-inflated negative binomial regression with MLRT techniques: An approach to evaluating shipping accident casualties." Ocean Engineering 166 (October 2018): 135–44. http://dx.doi.org/10.1016/j.oceaneng.2018.08.011.

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