Results 21 to 30 of about 4,654,667 (290)

On a Characterization of Zero-Inflated Negative Binomial Distribution

Open Journal of Statistics, 2015
Zero-inflated negative binomial distribution is characterized in this paper through a linear differential equation satisfied by its probability generating function.
R. Suresh, G. Nanjundan, S. Nagesh, Sadiq Pasha   +3 more
openaire   +2 more sources

Bayesian multilevel zero-inflated negative binomial regression: Twitter mentions.

, 2022
Bayesian multilevel zero-inflated negative binomial regression: Twitter mentions.
Jing Zeng (105826), Mike S. Schäfer (7238972), Chung-hong Chan (9191446)   +2 more
core   +1 more source

Zero-inflated negative binomial regression model simultaneously predicting selective exposure to and selective avoidance of populist news features.

, 2021
Zero-inflated negative binomial regression model simultaneously predicting selective exposure to and selective avoidance of populist news features.
Sebastian Scherr (5785856), Dominik Leiner (11534619)   +1 more
core   +1 more source

Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model. [PDF]

BMC Infect Dis
Abstract Background It is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index (BSI) in the early warning and predicting the epidemic trend of COVID-19.
Zhou W, Huang D, Liang Q, Huang T, Wang X, Pei H, Chen S, Liu L, Wei Y, Qin L, Xie Y.   +10 more
europepmc   +4 more sources

Priors for the logistic regression model, the zero-inflated negative binomial model, and the negative binomial model.

, 2023
Priors for the logistic regression model, the zero-inflated negative binomial model, and the negative binomial model.
Clara Pencer (9459661), Kiran Wadhawan (16955719), Martin Krkošek (197141), Devin Kirk (4836105), Madison Hönig (16955716), Leila Krichel (9459658)   +5 more
core   +1 more source

Estimating the burden of malaria in Senegal : Bayesian zero-inflated binomial geostatistical modeling of the MIS 2008 data [PDF]

, 2012
The Research Center for Human Development in Dakar (CRDH) with the technical assistance of ICF Macro and the National Malaria Control Programme (NMCP) conducted in 2008/2009 the Senegal Malaria Indicator Survey (SMIS), the first nationally representative
Mame Birame Diouf (179715), Diouf Mame Birame, Gaye Oumar, Gosoniu, L., Lassana Konate, Penelope Vounatsou, Diouf, M. B., Giardina Federica, Perry, R., Vounatsou Penelope, Laura Gosoniu, Vounatsou, P., Konate Lassana, Ousmane Faye (169435), Gosoniu Laura, Giardina, F., Oumar Gaye (32489), Perry Robert, Lassana Konate (37448), Federica Giardina (179708), Federica Giardina, Faye Ousmane, Penelope Vounatsou (49253), Oumar Gaye, Robert Perry (179720), Faye, O., Gaye, O., Mame Birame Diouf, Ousmane Faye, Laura Gosoniu (179711), Konate, L., Robert Perry, Gaye O, Gosoniu L, Faye O, Konate L, Diouf MB, Vounatsou P, Giardina F, Perry R   +39 more
core   +1 more source

Summary of model fits for the logistic regression model, the zero-inflated negative binomial model, and the negative binomial model.

, 2023
Summary of model fits for the logistic regression model, the zero-inflated negative binomial model, and the negative binomial model.
Clara Pencer (9459661), Kiran Wadhawan (16955719), Martin Krkošek (197141), Devin Kirk (4836105), Madison Hönig (16955716), Leila Krichel (9459658)   +5 more
core   +1 more source

Pemodelan Pneumonia Berat Menggunakan Regresi Zero Inflated Negative Binomial di Gorontalo [PDF]

, 2022
In certain cases, the response variable has an excess zero that causes overdispersion. Therefore, to overcome overdispersion because excess zero Zero-inflated negative binomial regression can be used.
Achmad, Novianita, Payu, Muhammad Rezky Friesta, Rahim, Yolanda   +2 more
core   +1 more source

Neutrosophic Negative Binomial Hurdle Models for Overdispersed Zero-Inflated Count Data

Transactions on Fuzzy Sets and Systems
The Negative Binomial Hurdle (NBH) model stands as a preeminent statistical framework for analyzing overdispersed count data characterized by a preponderance of zero observations.
Ibrahim Sadok
doaj   +2 more sources

Comparison of Test Statistic for Zero-Inflated Negative Binomial against Zero-Inflated Poisson Model

Indian Journal of Science and Technology, 2015
In this study, the existence of score test and alternative tests were studied for testing the overdispersion parameter after including covariates in ZINB against ZIP models. The power of the three tests for different degrees of overdispersion parameter and various sample sizes were also obtained through Monte Carlo simulation.
B. Muniswamy, Dejen Tesfaw Molla, N. Konda Reddy   +2 more
openaire   +3 more sources