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Journal articles on the topic 'Zero-and-One Inflated Beta Regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Liu, Fang, and Yunchuan Kong. "zoib: An R Package for Bayesian Inference for Beta Regression and Zero/One Inflated Beta Regression." R Journal 7, no. 2 (2015): 34. http://dx.doi.org/10.32614/rj-2015-019.

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Ospina, Raydonal, and Silvia L. P. Ferrari. "A general class of zero-or-one inflated beta regression models." Computational Statistics & Data Analysis 56, no. 6 (2012): 1609–23. http://dx.doi.org/10.1016/j.csda.2011.10.005.

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3

Liu, Fang, and Evercita C. Eugenio. "A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression." Statistical Methods in Medical Research 27, no. 4 (2016): 1024–44. http://dx.doi.org/10.1177/0962280216650699.

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Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather

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4

Abdel-Karim, Amany Hassan. "Extended zero-one inflated beta and adjusted three-part regression models for proportional data analysis." Communications in Statistics - Simulation and Computation 46, no. 8 (2016): 6155–72. http://dx.doi.org/10.1080/03610918.2016.1197248.

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Ríos-Pena, Laura, Thomas Kneib, Carmen Cadarso-Suárez, Nadja Klein, and Manuel Marey-Pérez. "Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression." Environmental Modelling & Software 110 (December 2018): 107–18. http://dx.doi.org/10.1016/j.envsoft.2018.03.008.

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Aguirre-Larracoechea, Urko, and Cruz E. Borges. "Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches." Mathematics 9, no. 17 (2021): 2081. http://dx.doi.org/10.3390/math9172081.

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Real-life data are bounded and heavy-tailed variables. Zero-one-inflated beta (ZOIB) regression is used for modelling them. There are no appropriate methods to address the problem of missing data in repeated bounded outcomes. We developed an imputation method using ZOIB (i-ZOIB) and compared its performance with those of the naïve and machine-learning methods, using different distribution shapes and settings designed in the simulation study. The performance was measured employing the absolute error (MAE), root-mean-square-error (RMSE) and the unscaled mean bounded relative absolute error (UMBR

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Zhang, Xinyan, Boyi Guo, and Nengjun Yi. "Zero-Inflated gaussian mixed models for analyzing longitudinal microbiome data." PLOS ONE 15, no. 11 (2020): e0242073. http://dx.doi.org/10.1371/journal.pone.0242073.

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Motivation The human microbiome is variable and dynamic in nature. Longitudinal studies could explain the mechanisms in maintaining the microbiome in health or causing dysbiosis in disease. However, it remains challenging to properly analyze the longitudinal microbiome data from either 16S rRNA or metagenome shotgun sequencing studies, output as proportions or counts. Most microbiome data are sparse, requiring statistical models to handle zero-inflation. Moreover, longitudinal design induces correlation among the samples and thus further complicates the analysis and interpretation of the micro

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Di Brisco, Agnese Maria, Sonia Migliorati, and Andrea Ongaro. "Robustness against outliers: A new variance inflated regression model for proportions." Statistical Modelling 20, no. 3 (2019): 274–309. http://dx.doi.org/10.1177/1471082x18821213.

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This article addresses the issue of building regression models for bounded responses, which are robust in the presence of outliers. To this end, a new distribution on (0,1) and a regression model based on it are proposed and some properties are derived. The distribution is a mixture of two beta components. One of them, showing a higher variance (variance inflated) is expected to capture outliers. Within a Bayesian approach, an extensive robustness study is performed to compare the new model with three competing ones present in the literature. A broad range of inferential tools are considered,

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Wohlgemuth, Murilo, Carlos Ernani Fries, Ângelo Márcio Oliveira Sant’Anna, Ricardo Giglio, and Diego Castro Fettermann. "Assessment of the technical efficiency of Brazilian logistic operators using data envelopment analysis and one inflated beta regression." Annals of Operations Research 286, no. 1-2 (2018): 703–17. http://dx.doi.org/10.1007/s10479-018-3105-7.

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Wang, Yi (Victor), Paolo Gardoni, Colleen Murphy, and Stéphane Guerrier. "Predicting Fatality Rates Due to Earthquakes Accounting for Community Vulnerability." Earthquake Spectra 35, no. 2 (2019): 513–36. http://dx.doi.org/10.1193/022618eqs046m.

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The existing prediction models for earthquake fatalities usually require a detailed building inventory that might not be readily available. In addition, existing models tend to overlook the socioeconomic characteristics of communities of interest as well as zero-fatality data points. This paper presents a methodology that develops a probabilistic zero-inflated beta regression model to predict earthquake fatality rates given the geographic distributions of earthquake intensities with data reflecting community vulnerability. As an illustration, the prediction model is calibrated using fatality d

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11

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

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Composite endpoints are commonplace in biomedical research. The complex nature of many health conditions and medical interventions demand that composite endpoints be employed. Different approaches exist for the analysis of composite endpoints. A Monte Carlo simulation study was employed to assess the statistical properties of various regression methods for analyzing binary composite endpoints. We also applied these methods to data from the BETTER trial which employed a binary composite endpoint. We demonstrated that type 1 error rates are poor for the Negative Binomial regression model and the

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Csurilla, Gergely, András Gyimesi, Erika Kendelényi-Gulyás, and Tamás Sterbenz. "Where is victory most certain? The level of luck-based noise factor in Summer Olympic Games." Acta Oeconomica 71, no. 3 (2021): 369–86. http://dx.doi.org/10.1556/032.2021.00018.

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Abstract We describe a statistical approach for the measurement of the newly defined luck-based noise factor in sports. It is defined as the difference between the actual outcome and the expected outcome based on the model predictions. We raise the question whether some sports exhibit a higher level of noise-factor than others, making investments in that sport riskier. Data from 14 individual sports in six Summer Olympic Games between 1996 and 2016 were included in the analysis. Market shares are predicted by the autoregressive linear and zero-inflated beta regression models with exogenous var

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Liu, Jiayi, Yuqing Li, Yi Feng, et al. "Patterned progression of gut microbiota associated with necrotizing enterocolitis and late onset sepsis in preterm infants: a prospective study in a Chinese neonatal intensive care unit." PeerJ 7 (July 22, 2019): e7310. http://dx.doi.org/10.7717/peerj.7310.

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Necrotizing enterocolitis (NEC) and late-onset sepsis (LOS) are two common premature birth complications with high morbidity and mortality. Recent studies in Europe and America have linked gut microbiota dysbiosis to their etiology. However, similar studies in Asian populations remain scant. In this pilot study, we profiled gut microbiota of 24 Chinese preterm infants from birth till death or discharge from NICU. Four of them developed NEC and three developed LOS. Unexpectedly, we detected highly-diversified microbiota with similar compositions in all patients shortly after birth. However, as

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Zouiouich, Semi, Erikka Loftfield, Inge Huybrechts, et al. "Markers of metabolic health and gut microbiome diversity: findings from two population-based cohort studies." Diabetologia 64, no. 8 (2021): 1749–59. http://dx.doi.org/10.1007/s00125-021-05464-w.

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Abstract Aims/hypothesis The gut microbiome is hypothesised to be related to insulin resistance and other metabolic variables. However, data from population-based studies are limited. We investigated associations between serologic measures of metabolic health and the gut microbiome in the Northern Finland Birth Cohort 1966 (NFBC1966) and the TwinsUK cohort. Methods Among 506 individuals from the NFBC1966 with available faecal microbiome (16S rRNA gene sequence) data, we estimated associations between gut microbiome diversity metrics and serologic levels of HOMA for insulin resistance (HOMA-IR)

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15

Luo, Rong, and Sudhir Paul. "Estimation for zero-inflated beta-binomial regression model with missing response data." Statistics in Medicine 37, no. 26 (2018): 3789–813. http://dx.doi.org/10.1002/sim.7845.

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Astuti, Cindy Cahyaning, and Angga Dwi Mulyanto. "Estimation Parameters And Modelling Zero Inflated Negative Binomial." CAUCHY 4, no. 3 (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 wit

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Santos, Bruno, and Heleno Bolfarine. "Bayesian analysis for zero-or-one inflated proportion data using quantile regression." Journal of Statistical Computation and Simulation 85, no. 17 (2014): 3579–93. http://dx.doi.org/10.1080/00949655.2014.986733.

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18

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 (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

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19

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 (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

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20

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 (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 a

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21

Liu, Pengyi, Kam Chuen Yuen, Liu-Cang Wu, Guo-Liang Tian, and Tao Li. "Zero-one-inflated simplex regression models for the analysis of continuous proportion data." Statistics and Its Interface 13, no. 2 (2020): 193–208. http://dx.doi.org/10.4310/sii.2020.v13.n2.a5.

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22

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 group

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

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Zero Inflated Generalized Poisson (ZIGP) is a regression model used to analyze Poisson distributed discrete data which contains mostly zero and tends to experience overdispersion (varians value greater than the mean value). The purpose of this research is to find out the best model and the factors which influence the maternal mortality in Bali Province in year 2016 by using ZIGP regression model. The data used in this research was data from health profile Bali Province with the object totally 57 district rate data has proportion of zeros value more than 50% on the response variable. The analys

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Martínez-Flórez, Guillermo, Hector W. Gomez, and Roger Tovar-Falón. "Modeling Proportion Data with Inflation by Using a Power-Skew-Normal/Logit Mixture Model." Mathematics 9, no. 16 (2021): 1989. http://dx.doi.org/10.3390/math9161989.

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Rate or proportion data are modeled by using a regression model. The considered regression model can be used for studying phenomena with a response on the (0, 1), [0, 1), (0, 1], or [0, 1] intervals. To connect the response variable with the linear predictor in the regression model, we use a logit link function, which guarantees that the obtained prediction ranges between zero and one in the cases inflated at zero or one (or both). The model is complemented with the assumption that the errors follow a power-skew-normal distribution, resulting in a very flexible model, and with a non-singular i

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Pinheiro, Hildete P., Rafael P. Maia, Eufrásio A. Lima Neto, and Mariana Rodrigues-Motta. "Zero-one augmented beta and zero-inflated discrete models with heterogeneous dispersion for the analysis of student academic performance." Statistical Methods & Applications 28, no. 4 (2019): 749–67. http://dx.doi.org/10.1007/s10260-019-00455-z.

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26

Akbarzadeh Baghban, Alireza, Asma Pourhoseingholi, Farid Zayeri, Ali Akbar Jafari, and Seyed Moayed Alavian. "Application of Zero-Inflated Poisson Mixed Models in Prognostic Factors of Hepatitis C." BioMed Research International 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/403151.

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Background and Objectives. In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C.Methods. The data was collected from a longitudinal study during 2005–2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the prop

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Menezes, André F. B., Josmar Mazucheli, and Marcelo Bourguignon. "A parametric quantile regression approach for modelling zero‐or‐one inflated double bounded data." Biometrical Journal 63, no. 4 (2021): 841–58. http://dx.doi.org/10.1002/bimj.202000126.

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Rahayu, Lili Puspita, Kusman Sadik, and Indahwati Indahwati. "Overdispersion study of poisson and zero-inflated poisson regression for some characteristics of the data on lamda, n, p." International Journal of Advances in Intelligent Informatics 2, no. 3 (2016): 140. http://dx.doi.org/10.26555/ijain.v2i3.73.

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Poisson distribution is one of discrete distribution that is often used in modeling of rare events. The data obtained in form of counts with non-negative integers. One of analysis that is used in modeling count data is Poisson regression. Deviation of assumption that often occurs in the Poisson regression is overdispersion. Cause of overdispersion is an excess zero probability on the response variable. Solving model that be used to overcome of overdispersion is zero-inflated Poisson (ZIP) regression. The research aimed to develop a study of overdispersion for Poisson and ZIP regression on some

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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 (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 censo

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Xiao, Xiang, Yincai Tang, Ancha Xu, and Guoqiang Wang. "Bayesian inference for zero-and-one-inflated geometric distribution regression model using Pólya-Gamma latent variables." Communications in Statistics - Theory and Methods 49, no. 15 (2020): 3730–43. http://dx.doi.org/10.1080/03610926.2019.1709647.

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MARDIANI, LUH KOMANG, KOMANG GDE SUKARSA, and I. GUSTI AYU MADE SRINADI. "PENERAPAN REGRESI ZERO INFLATED POISSON UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON (Studi Kasus: Ketidaklulusan Siswa SMA/MA dalam Ujian Nasional di Buleleng)." E-Jurnal Matematika 2, no. 3 (2013): 23. http://dx.doi.org/10.24843/mtk.2013.v02.i03.p044.

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The Poisson regression analysis is one of the regression methods used for count data and has the assumption of equidispersion. However, it is the overdispersion and then underestimate standard errors will be obtained. If the data are overdispersed and more data is zero then ZIP (Zero Inflated Regression) regression is used. ZIP regression model is more appropriate to be used to analyze the amount of Senior High School/Madrasah Aliyah who do not pass the exam with five independent variables, because a lot of data failure is zero. In this paper, data are overdispersed on Poisson regression, so Z

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32

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 (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 th

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Bracamontes, Christina G., Thelma Carrillo, Jane Montealegre, Leonid Fradkin, Michele Follen, and Zuber D. Mulla. "Analysis of count data in the setting of cervical cancer detection." Journal of Investigative Medicine 68, no. 6 (2020): 1196–98. http://dx.doi.org/10.1136/jim-2020-001381.

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Women with an abnormal Pap smear are often referred to colposcopy, a procedure during which endocervical curettage (ECC) may be performed. ECC is a scraping of the endocervical canal lining. Our goal was to compare the performance of a naïve Poisson (NP) regression model with that of a zero-inflated Poisson (ZIP) model when identifying predictors of the number of distress/pain vocalizations made by women undergoing ECC. Data on women seen in the colposcopy clinic at a medical school in El Paso, Texas, were analyzed. The outcome was the number of pain vocalizations made by the patient during EC

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Seidel, E. J., J. B. Pazini, V. L. D. Tomazella, et al. "Predicting Rice Stem Stink Bug Population Dynamics Based on GAMLSS Models." Environmental Entomology 49, no. 5 (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

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Annisa, Yusrina Ratna, Jajang Jajang, and Agus Sugandha. "PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI KEMATIAN IBU MELAHIRKAN DI JAWA TENGAH DENGAN ZERO INFLATED POISSON REGRESSION." Jurnal Ilmiah Matematika dan Pendidikan Matematika 12, no. 2 (2021): 1. http://dx.doi.org/10.20884/1.jmp.2020.12.2.3615.

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One of the targets of the Sustainable Development Goals (SDGs) is to reduce the maternal mortality rate by 2030. In reducing the maternal mortality rate, it is necessary to first study the factors that influence it. The number of maternal mortality rate in Central Java Province in 2018 was a discrete random variable that had a Poisson distribution and had many zero values. Generally, a model was used to counting data is Poisson regression model. However, the maternal mortality that has many zero values caused overdispersion. Therefore, Poisson model was not relevant in that case. In this paper

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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 (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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Xu, Chengcheng, Shuoyan Xu, Chen Wang, and Jing Li. "Investigating the factors affecting secondary crash frequency caused by one primary crash using zero-inflated ordered probit regression." Physica A: Statistical Mechanics and its Applications 524 (June 2019): 121–29. http://dx.doi.org/10.1016/j.physa.2019.03.036.

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TSELONI, ANDROMACHI, IOANNIS NTZOUFRAS, ANNA NICOLAOU, and KEN PEASE. "Concentration of personal and household crimes in England and Wales." European Journal of Applied Mathematics 21, no. 4-5 (2010): 325–48. http://dx.doi.org/10.1017/s0956792510000057.

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Crime is disproportionally concentrated in few areas. Though long established, there remains uncertainty about the reasons for variation in the concentration of similar crime (repeats) or different crime (multiples). Wholly neglected have been composite crimes when more than one crime types coincide as parts of a single event. The research reported here disentangles area crime concentration into repeats, multiple and composite crimes. The results are based on estimated bivariate zero-inflated Poisson regression models with covariance structure which explicitly account for crime rarity and crim

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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) (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 s

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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 (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 s

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Profeta, Adriano, and Ulrich Hamm. "Who cares about local feed in local food products? Results from a consumer survey in Germany." British Food Journal 121, no. 3 (2019): 711–24. http://dx.doi.org/10.1108/bfj-07-2018-0446.

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Purpose The purpose of this paper is to analyse if German consumers are willing-to-pay a price premium for local food produced with local feed. The study provides insights into reasons explaining consumer preferences for animal products produced with local feed. Design/methodology/approach Computer self-assisted personal interviews (CASI) with 1,602 German consumers were conducted. To calculate the price premium for local feed, consumers were asked about their willingness-to-pay (WTP) for local feed. The respondents had to indicate their WTP for the local feed share levels 75, 90 and 100 per c

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Timofeev, Nadia, Jacqueline N. Milton, Stephen W. Hartley, et al. "Genome-Wide Studies in Sickle Cell Anemia Show Associations Between SNPs in the Olfactory Receptor Gene Cluster and Fetal Hemoglobin Concentration." Blood 114, no. 22 (2009): 821. http://dx.doi.org/10.1182/blood.v114.22.821.821.

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Abstract Abstract 821 Fetal hemoglobin (HbF) is the major modulator of sickle cell anemia (SCA, homozygosity for HBB glu6val) severity. In a genome-wide association study in African Americans with SCA we sought to identify single nucleotide polymorphisms (SNPs) associated with HbF concentrations. A discovery sample of 848 African American subjects and a primary replication study of 305 subjects were examined. DNA was genotyped with the Illumina Human610-Quad SNP; some replication set samples were genotyped with the Sentrix HumanCNV370 or the 317K array. For quality control we excluded SNPs wit

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Nogarotto, Danilo Covaes, Caio Lucidius Naberezny Azevedo, and Jorge Luis Bazán. "Bayesian modeling and prior sensitivity analysis for zero–one augmented beta regression models with an application to psychometric data." Brazilian Journal of Probability and Statistics 34, no. 2 (2020): 304–22. http://dx.doi.org/10.1214/18-bjps423.

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Wei, Melissa Y., Nicholas Tilton, and Kenneth J. Mukamal. "QUANTIFYING THE BURDEN OF HOSPITALIZED DAYS IN MEDICARE BENEFICIARIES WITH MULTIMORBIDITY." Innovation in Aging 3, Supplement_1 (2019): S924. http://dx.doi.org/10.1093/geroni/igz038.3365.

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Abstract Multimorbidity predicts several health outcomes including physical and cognitive functioning and mortality. Multimorbidity also predicts healthcare burden, but this has not been studied using a patient-centered measure that weights conditions by their impact on physical functioning. Health and Retirement Study participants were continuously enrolled in Medicare Parts A/B 1-year before and after the 2012-2013 HRS interview. Medicare claims were used to compute ICD-coded multimorbidity-weighted index (MWI-ICD) by summing physical functioning-weighted conditions. Given excess observation

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Min, Lee, So, and Cho. "Deforestation Increases the Risk of Scrub Typhus in Korea." International Journal of Environmental Research and Public Health 16, no. 9 (2019): 1518. http://dx.doi.org/10.3390/ijerph16091518.

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Background: Scrub typhus is an important public health issue in Korea. Risk factors for scrub typhus include both individual-level factors and environmental drivers, and some are related to the increased density of vector mites and rodents, the natural hosts of the mites. In this regard, deforestation is a potential risk factor, because the deforestation-induced secondary growth of scrub vegetation may increase the densities of mites and rodents. To examine this hypothesis, this study investigated the association between scrub typhus and deforestation. Methods: We acquired district-level data

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Gemma, Marco, Fulvia Pennoni, Roberta Tritto, and Massimo Agostoni. "Risk of adverse events in gastrointestinal endoscopy: Zero-inflated Poisson regression mixture model for count data and multinomial logit model for the type of event." PLOS ONE 16, no. 6 (2021): e0253515. http://dx.doi.org/10.1371/journal.pone.0253515.

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Background and aims We analyze the possible predictive variables for Adverse Events (AEs) during sedation for gastrointestinal (GI) endoscopy. Methods We consider 23,788 GI endoscopies under sedation on adults between 2012 and 2019. A Zero-Inflated Poisson Regression Mixture (ZIPRM) model for count data with concomitant variables is applied, accounting for unobserved heterogeneity and evaluating the risks of multi-drug sedation. A multinomial logit model is also estimated to evaluate cardiovascular, respiratory, hemorrhagic, other AEs and stopping the procedure risk factors. Results In 7.55% o

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Paliwal, Yoshita, Patricia W. Slattum, and Scott M. Ratliff. "Chronic Health Conditions as a Risk Factor for Falls among the Community-Dwelling US Older Adults: A Zero-Inflated Regression Modeling Approach." BioMed Research International 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5146378.

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Falls are an important health concern among older adults due to age-related changes in the body. Having a medical history of chronic health condition may pose even higher risk of falling. Only few studies have assessed a number of chronic health conditions as risk factor for falls over a large nationally representative sample of US older adults. In this study, Behavioral Risk Factor Surveillance System (BRFSS) 2014 participants aged 65 years and older (n = 159,336) were evaluated. It was found that 29.7% (n=44,550) of the sample experienced at least one fall and 16.3% (n=20,444) experienced mo

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Kharroubi, Samer A. "Analysis of SF-6D Health State Utility Scores: Is Beta Regression Appropriate?" Healthcare 8, no. 4 (2020): 525. http://dx.doi.org/10.3390/healthcare8040525.

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Background: Typically, modeling of health-related quality of life data is often troublesome since its distribution is positively or negatively skewed, spikes at zero or one, bounded and heteroscedasticity. Objectives: In the present paper, we aim to investigate whether Bayesian beta regression is appropriate for analyzing the SF-6D health state utility scores and respondent characteristics. Methods: A sample of 126 Lebanese members from the American University of Beirut valued 49 health states defined by the SF-6D using the standard gamble technique. Three different models were fitted for SF-6

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Eric, Chinaeke, Gwynn Melanie, Hong Yuan, Zhang Jiajia, and Olatosi Bankole. "The positive association between employment and self-reported mental health in the USA: a robust application of marginalized zero-inflated negative binomial regression (MZINB)." Journal of Public Health 42, no. 2 (2020): 340–52. http://dx.doi.org/10.1093/pubmed/fdaa030.

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Abstract Background Few studies have assessed the impact of employment on mental health among chronically ill patients. This study investigated the association between employment and self-reported mental unhealthy days among US adults. Methods For this cross-sectional cohort study, we pooled 2011–2017 Behavioral Risk Factor Surveillance System (BRFSS) survey data. We examined the association between employment and mental health in nine self-reported chronic conditions using marginalized zero-inflated negative binomial regression (MZINB). All analyses were conducted using SAS statistical softwa

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Bandyopadhyay, Dipankar, Diana M. Galvis, and Victor H. Lachos. "Augmented mixed models for clustered proportion data." Statistical Methods in Medical Research 26, no. 2 (2014): 880–97. http://dx.doi.org/10.1177/0962280214561093.

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Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a

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