The impact of political instability upon the increase of terrorism in the Middle East

Severe Form ◽

Political Regime ◽

Level Of Education ◽

Binomial Regression ◽

Different Types ◽

The key goal of the article is to examine whether the domestic political instability associated with the “Arab Spring” caused the subsequent surge of global terrorism, which reached its peak in 2014. The author reviews six different types of domestic political instability: antigovernment demonstrations, national strikes, government crises, government repression, disturbances, and revolutions. Using the regression models, the author clarifies the impact of such factors as the level of education, Internet access, economic development, democratization indexes, and the degree of religious and ethnic fragmentariness. Analysis is conducted on the results of the models separately for different types of political regimes, forms of domestic political instability, and global regions. The results of construction and analysis a number of negative binomial regression models testify to the support of “escalation effect”, which implies that heightened intensity of domestic political instability leads to the surge of terrorist attacks. More severe forms of domestic political instability, namely repression and disturbances, generate a higher level of terrorism; however, revolution, as the most severe form of domestic political instability does not produce such effect. The formulated conclusions are also substantiated by the fact that certain forms of political instability have a different impact upon terrorism and its peculiarities, depending on the geographical region and the type of political regime.

The Impact of the COVID-19 Pandemic on Cross-Border Mergers and Acquisitions’ Determinants: New Empirical Evidence from Quasi-Poisson and Negative Binomial Regression Models

Economies ◽

10.3390/economies9040184 ◽

2021 ◽

Vol 9 (4) ◽

pp. 184

Author(s):

Han-Sol Lee ◽

Ekaterina A. Degtereva ◽

Alexander M. Zobov

Keyword(s):

Mergers And Acquisitions ◽

Empirical Evidence ◽

Regression Models ◽

Negative Binomial ◽

Entry Modes ◽

Cross Sectional ◽

Cross Border ◽

Binomial Regression ◽

The cross-border movement of capital has suffered due to the COVID-19 pandemic since December 2019. Nevertheless, it is unrealistic for multinational companies to withdraw giant global value chains (GVCs) overnight because of the pandemic. Instead, active discussions and achievements of deals in cross-border mergers and acquisitions (M&As) are expected in the post-COVID-19 era among various other market entry modes, considering the growing demand in high technologies in societies. This paper analyzes particular determinants of cross-border mergers and acquisitions (M&As) during the pandemic year (2020) based on cross-sectional datasets by employing quasi-Poisson and negative binomial regression models. According to the empirical evidence, COVID-19 indices do not hamper M&A deals in general. This indicates that managerial capabilities of the coronavirus, not the outbreak itself, determined locational decisions of M&A deals during the pandemic. In this vein, it is expected that the vaccination rate will become a key factor of locational decision for M&A deals in the near future. Furthermore, countries that have been outstanding in coping with COVID-19 and thus serve as a good example for other nations may seize more opportunities to take a leap forward. In addition, as hypothesized, the results present positive and significant associations with M&A deals and the SDG index, confirming the resource-based theory of internationalization. In particular, the achievement of SDGs seems to exercise much influence in developing countries for M&A bidders during the pandemic year. This indicates that the pandemic demands a new zeitgeist that pursues growth while resolving existing but disregarded environmental issues and cherishes humanitarian values, for all countries, non-exceptionally, standing at the start line of the post-COVID-19 era.

Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models

BMC Public Health ◽

10.1186/s12889-015-1500-4 ◽

2015 ◽

Vol 15 (1) ◽

Cited By ~ 5

Author(s):

Ta-Chien Chan ◽

Yung-Chu Teng ◽

Jing-Shiang Hwang

Keyword(s):

Regression Models ◽

Negative Binomial ◽

Influenza Like Illness ◽

Pearson Residuals ◽

Managing Inflation

Crime & Delinquency ◽

10.1177/0011128716679796 ◽

2016 ◽

Vol 63 (1) ◽

pp. 77-87 ◽

Cited By ~ 5

Author(s):

William H. Fisher ◽

Stephanie W. Hartwell ◽

Xiaogang Deng

Keyword(s):

Count Data ◽

Regression Models ◽

Negative Binomial ◽

Excess Zeros ◽

Binomial Regression ◽

Negative Binomial Models ◽

Using Data ◽

Over Dispersion ◽

Binomial Models

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.

Expressway Traffic Accidents Involving Human Injuries and Fatalities in Ethiopia: Negative Binomial Regression Model

10.21203/rs.3.rs-151181/v1 ◽

2021 ◽

Author(s):

Yesuf Abdela Mustefa ◽

Addis Belayhun

Keyword(s):

Regression Model ◽

Traffic Accident ◽

Traffic Accidents ◽

Negative Binomial ◽

Road Traffic ◽

Weather Condition ◽

Negative Binomial Regression Model ◽

Level Of Education ◽

Abstract Background: Road traffic accident is a major public health as well as economic challenge that rated the eighth leading cause of death. The severity became higher in developing countries. Ethiopian is among the most confronted countries in the world. We utilized the Ethiopian Toll Roads Enterprise data to provide insights and model significant determinants of accidents involving injuries and fatalities. Besides utilizing recent dataset, we applied the most appropriate but forwent statistical model. Moreover, we examined the significance of the effects of drivers’ age and gender that have not been the cases in the literatures.Methods: We made descriptive insights available on the basis of graphs from integrated traffic accident and flow datasets. We tested for the presence of over-dispersion in a total of 1824 observations of accident data recorded from September, 2014 to December, 2019 for inferential analysis. Finally, we modeled the effects of significant variables on the number of injuries using the negative binomial regression model. Results: we found that the number of injuries in accidents were significantly determined by type of vehicles, ownership status of vehicles, accident time weather condition, driver-vehicle relationship, drivers’ level of education, and drivers’ age.Conclusions: Heavy trucks were more likely to cause more number of injuries than medium or small vehicles. Hot and windy weather conditions were associated with higher probability of the number of injuries. The likelihood of the number of injuries were lower when drivers are owner of the vehicle; drivers level of education is above secondary school; and the age of the driver is between 18 and 23 years old. Moreover, due concern needs to be given for traffic road rules.

Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

Statistics in Medicine ◽

10.1002/sim.7804 ◽

2018 ◽

Vol 37 (20) ◽

pp. 3012-3026 ◽

Cited By ~ 2

Author(s):

Saptarshi Chatterjee ◽

Shrabanti Chowdhury ◽

Himel Mallick ◽

Prithish Banerjee ◽

Broti Garai

Keyword(s):

Health Care ◽

Regression Models ◽

Negative Binomial ◽

Health Care Demand ◽

6. Poisson and Negative Binomial Regression Models

Regression Models for Categorical, Count, and Related Variables ◽

10.1525/9780520965492-008 ◽

2019 ◽

pp. 131-158

Keyword(s):

Regression Models ◽

Negative Binomial ◽

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

Remote Sensing ◽

10.3390/rs11171958 ◽

2019 ◽

Vol 11 (17) ◽

pp. 1958 ◽

Cited By ~ 8

Author(s):

Hanlin Zhou ◽

Lin Liu ◽

Minxuan Lan ◽

Bo Yang ◽

Zengli Wang

Keyword(s):

Negative Binomial ◽

Infrared Imaging ◽

Negative Impact ◽

Positive Impact ◽

Information Criterion ◽

Binomial Regression ◽

Street Robbery ◽

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.

What Do We Know About Changing Economic Activity of Firms?

Studies in Microeconomics ◽

10.1177/2321022218869790 ◽

2019 ◽

pp. 232102221886979

Author(s):

Radhika Pandey ◽

Amey Sapre ◽

Pramod Sinha

Keyword(s):

Economic Activity ◽

Regression Models ◽

Statistical Approach ◽

Negative Binomial ◽

Large Data ◽

Poisson Model ◽

Data Set ◽

Binomial Regression ◽

History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

Traffic accident modeling: some statistical issues

Canadian Journal of Civil Engineering ◽

10.1139/l06-056 ◽

2006 ◽

Vol 33 (9) ◽

pp. 1115-1124 ◽

Cited By ~ 53

Author(s):

Z Sawalha ◽

T Sayed

Keyword(s):

Road Safety ◽

Regression Models ◽

Model Building ◽

Negative Binomial ◽

Prediction Models ◽

Outlier Analysis ◽

Accident Prediction ◽

Binomial Regression ◽

Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

Customer reactions to a webshop’s service quality

Empirica ◽

10.1007/s10663-019-09453-6 ◽

2019 ◽

Vol 47 (4) ◽

pp. 699-731

Author(s):

Franz Hackl ◽

Rudolf Winter-Ebmer

Keyword(s):

Service Quality ◽

Negative Binomial ◽

Data Set ◽

Product Categories ◽

Service Characteristics ◽

Binomial Regression ◽

Quality Aspects ◽

Quality Dimensions ◽

Abstract E-commerce has become an integral part of the world’s economy. In this study we investigate the impact of service quality in e-tailing on site visits and consumer demand. Such an analysis is important given the almost Bertrand-like competitive structure. Our analysis is based on a large representative data set obtained from a price comparison site covering essentially the complete Austrian e-tailing market. Customer evaluations for a broad range of 15 different service characteristics are condensed using factor analysis. Negative binomial regression analysis is used to measure the impact of service quality dimensions on referral requests to online shops for different product categories. Our results show that the most important service quality aspects are those related to the ordering process and the firm’s website performance.