scholarly journals 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

2021 ◽  
Vol 17 (3) ◽  
pp. 357-369
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Tobacco Control ◽  
Negative Binomial ◽  
Adult Population ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Cigarette Consumption ◽  
Consumption Data ◽  
Comparison Results ◽  
Binomial Regression ◽  
Hurdle Regression

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.

scholarly journals Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

2019 ◽  
Vol 11 (17) ◽  
pp. 1958 ◽  
Author(s):  
Hanlin Zhou ◽  
Lin Liu ◽  
Minxuan Lan ◽  
Bo Yang ◽  
Zengli Wang
Keyword(s):  
Negative Binomial ◽  
Infrared Imaging ◽  
Negative Impact ◽  
Positive Impact ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Binomial Regression ◽  
Street Robbery ◽  
The Impact

Previous research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight.

scholarly journals A Study of Count Regression Models for Mortality Rate

CAUCHY ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 142-151
Author(s):  
Anwar Fitrianto
Keyword(s):  
Mortality Rate ◽  
Regression Model ◽  
Count Data ◽  
Bayesian Information Criterion ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Poisson Regression Model ◽  
Binomial Regression

This paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data. The method used is comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to find out which method suits the data the most. The results show that the data indeed display higher variability. Among the three models, the model preferred is NEGBIN 1 model.

Infrastructure and Operational Influences on Collisions Between Trams and Left-Turning Cars

2017 ◽  
Vol 2648 (1) ◽  
pp. 10-22
Author(s):  
Christian M. Marti ◽  
Ambra Toletti ◽  
Seraina Tresch ◽  
Ulrich Weidmann
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Research Approach ◽  
Data Set ◽  
Left Turn ◽  
Binomial Regression ◽  
Traffic Volumes ◽  
The Right ◽  
Predictor Factors

This research identified infrastructural and operational factors that influenced the most common type of car–tram collision: cars making opposing turns in front of trams. Few studies have analyzed influences on car–tram collisions quantitatively, but none have explored predictor factors for opposing-turn crashes—a research gap addressed with this paper. The two largest Swiss tram networks, Basel and Zurich, were used for the analysis. A point-based research approach was chosen: all locations within a tram network at which a car could turn left (an opposing turn where traffic drives on the right) in front of a tram were identified. For each of these points, data on dependent and predictor variables were collected. This data set was analyzed with Poisson, negative binomial, and zero-inflated negative binomial regression models. The number of left-turning car–tram collisions was used as the dependent variable, while predictors were derived from a literature review; models were fitted by using all predictors and with forward variable selection by means of Akaike’s information criterion. Traffic volumes (cars and trams), tram speed, and dedicated left-turn lanes were found to be significantly associated with a higher frequency of car–tram collisions, whereas turning left to access a service rather than a road, left-turn restrictions, proximity to a tram stop, and perpendicular turning angles were significantly associated with a lower frequency of left-turning car–tram collisions. On the basis of these results, left turns across tramways should be restricted for cars. Remaining conflict points should be located close to tram stops, have limited tram speed, and feature perpendicular turning angles.

scholarly journals Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression

2021 ◽  
Vol 10 (5) ◽  
pp. 286
Author(s):  
Ce Wang ◽  
Shuo Li ◽  
Jie Shan
Keyword(s):  
Geographically Weighted Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Random Parameter ◽  
Information Criterion ◽  
Weighted Regression ◽  
Spatial Methods ◽  
Spatial Approach ◽  
Binomial Regression ◽  
Superior Approach

Vehicle crashes on roads are caused by many factors. However, the influence of these factors is not necessarily homogenous across locations, which is a challenge for non-stationary modeling approaches. To address this problem, this paper adopts two types of methods allowing parameters to fluctuate among observations, that is, the random parameter approach and the geographically weighted regression (GWR) approach. With road curvature, curve length, pavement friction, and traffic volume as independent variables, vehicle crash frequencies are modeled by two non-spatial methods, including the negative binomial (NB) model and random parameter negative binomial (RPNB), as well as three spatial methods (GWR approach). These models are calibrated in microlevel using a dataset of 9415 horizontal curve segments with a total length of 1545 kilometers for a period of three years (2016–2018) over the State of Indiana. The results revealed that the GWR approach can capture spatial heterogeneity and therefore significantly outperforms the conventional non-spatial approach. Based on the Akaike Information Criterion (AICc), geographically weighted negative binomial regression (GWNBR) was proved to be a superior approach for statewide microlevel crash analysis.

Heterogeneity and Number of Export Destinations of Italian Firms: A Hurdle Negative Binomial Regression Approach

2013 ◽  
Vol 13 (03n04) ◽  
pp. 391-416 ◽  
Author(s):  
Maria Rosaria Ferrante ◽  
Marco Novelli
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Outcome Variable ◽  
Export Activity ◽  
Multiple Markets ◽  
Binomial Regression ◽  
Regression Approach ◽  
Excess Of Zeros ◽  
Hurdle Regression

This article addresses on an aspect of firms internationalization so far little explored, the choice of the number of export destinations and a proxy of the complexity of the export activity. As the outcome variable is a count with an excess of zeros, we use a hurdle regression model for count data that also allow disentangling the aspect of heterogeneity related to the decision to export from those measuring the number of markets served. Some differences arise by the comparison between the estimates regarding the propensity to export model and those of the model describing the number of export destinations. Regarding the propensity to export, the estimated models support the familiar evidences already presented in literature: exporters are larger, more productive, more innovative and invest more. With reference to the number of export destinations, it seems that not only the larger the number of markets served the more productive, large and willing to invest is the firm but also firms engaged in multiple markets seem to be older, financially stable, and willing to support organizational and managerial innovations.

scholarly journals Model Regresi Hurdle Negative Binomial (HNB) untuk Pemodelan Konsumsi Rokok di Provinsi Sulawesi Tengah

2021 ◽  
Vol 18 (1) ◽  
pp. 21-31
Author(s):  
D I Purnama
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Negative Binomial ◽  
Information Criterion ◽  
Cigarette Consumption ◽  
Education And Health ◽  
Consumption Data ◽  
Zip Model ◽  
Central Sulawesi ◽  
Average Expenditure

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.

Enhanced Methodology for the Identification of Locations with High Risk of Wet Crashes

2019 ◽  
Vol 2673 (11) ◽  
pp. 29-37
Author(s):  
Kenneth X. Vélez Rodríguez ◽  
Samer W. Katicha ◽  
Gerardo W. Flintsch
Keyword(s):  
Empirical Bayes ◽  
Negative Binomial ◽  
Water Film ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Limited Attention ◽  
Expected Number ◽  
Binomial Regression ◽  
Empirical Bayes Estimate ◽  
Grade Difference

About 18% of crashes on Virginia’s interstates from 2014 to 2016 were reported to be wet crashes. Although extensive research on crashes has been conducted, limited attention has been devoted to the prediction of wet crashes. The ratio of wet over dry crashes (wet over dry ratio [WDR]) has traditionally been the parameter of interest. In this paper, negative binomial regression is used to quantify the relationship between WDR and traffic and road parameters. One issue with the WDR is the handling of sites with zero dry crash counts. This was addressed by numerically replacing the zeros with 0.5 or by using an empirical Bayes estimate of the expected number of dry crashes instead of the dry crash counts. The empirical Bayes approach resulted in a better model fit as measured using Akaike’s Information Criterion. The negative binomial model developed for wet crashes was used to identify parameters that affect the pavement water film thickness and the expected number of wet crashes. The approach identified the longitudinal grade difference as an important parameter.

scholarly journals Is area deprivation associated with greater impacts of COVID-19 in care homes across England? A preliminary analysis of COVID-19 outbreaks and deaths

2021 ◽  
pp. jech-2020-215039 ◽  
Author(s):  
Anders Malthe Bach-Mortensen ◽  
Michelle Degli Esposti
Keyword(s):  
Negative Binomial ◽  
Care Home ◽  
Negative Binomial Regression ◽  
Population Characteristics ◽  
Care Homes ◽  
Future Research ◽  
Area Deprivation ◽  
Binomial Regression ◽  
Index Of Multiple Deprivation

IntroductionThe COVID-19 pandemic has disproportionately impacted care homes and vulnerable populations, exacerbating existing health inequalities. However, the role of area deprivation in shaping the impacts of COVID-19 in care homes is poorly understood. We examine whether area deprivation is linked to higher rates of COVID-19 outbreaks and deaths among care home residents across upper tier local authorities in England (n=149).MethodsWe constructed a novel dataset from publicly available data. Using negative binomial regression models, we analysed the associations between area deprivation (Income Deprivation Affecting Older People Index (IDAOPI) and Index of Multiple Deprivation (IMD) extent) as the exposure and COVID-19 outbreaks, COVID-19-related deaths and all-cause deaths among care home residents as three separate outcomes—adjusting for population characteristics (size, age composition, ethnicity).ResultsCOVID-19 outbreaks in care homes did not vary by area deprivation. However, COVID-19-related deaths were more common in the most deprived quartiles of IDAOPI (incidence rate ratio (IRR): 1.23, 95% CI 1.04 to 1.47) and IMD extent (IRR: 1.16, 95% CI 1.00 to 1.34), compared with the least deprived quartiles.DiscussionThese findings suggest that area deprivation is a key risk factor in COVID-19 deaths among care home residents. Future research should look to replicate these results when more complete data become available.

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