scholarly journals Beyond Reassurance: The Reputational Effect of Cultural Reforms in Peace Agreements

2022 ◽  
pp. 1-23
Author(s):  
Giuditta Fontana ◽  
Ilaria Masiero
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Peace Accord ◽  
Leadership Challenges ◽  
Civil Conflicts ◽  
Good Friday ◽  
Peace Agreements ◽  
Binomial Regression ◽  
Depth Analysis

Abstract We explore whether including cultural reforms in an intra-state peace accord facilitates its success. We distinguish between accommodationist and integrationist cultural provisions and employ a mixed research method combining negative binomial regression on a data set of all intra-state political agreements concluded between 1989 and 2017, and an in-depth analysis of the 1998 Good Friday Agreement for Northern Ireland. We recognize the important reassuring effect of accommodationist cultural reforms in separatist conflicts. However, we also find that they have an important and hitherto overlooked reputational effect across all conflict types. By enhancing the reputation of negotiating leaders, accommodationist cultural provisions contribute to ending violence by preventing leadership challenges, rebel fragmentation and remobilization across all civil conflicts. By the same logic, and despite the overwhelming emphasis of peace agreements on integrationist cultural initiatives, integrationist cultural reforms problematize leaders' ability to commit to pacts and to ensure compliance among their rank and file.

What Do We Know About Changing Economic Activity of Firms?

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 ◽  
Negative Binomial Regression ◽  
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

scholarly journals Customer reactions to a webshop’s service quality

Empirica ◽  
2019 ◽  
Vol 47 (4) ◽  
pp. 699-731
Author(s):  
Franz Hackl ◽  
Rudolf Winter-Ebmer
Keyword(s):  
Service Quality ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Product Categories ◽  
Service Characteristics ◽  
Binomial Regression ◽  
Quality Aspects ◽  
Quality Dimensions ◽  
The Impact

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.

scholarly journals Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 829
Author(s):  
Shuai Sun ◽  
Jun Bi ◽  
Montserrat Guillen ◽  
Ana M. Pérez-Marín
Keyword(s):  
Risk Score ◽  
Poisson Regression ◽  
High Speed ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Near Miss ◽  
Risk Scores ◽  
Data Set ◽  
Binomial Regression ◽  
Driving Risk

This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression (AICoverspeed = 997.0, BICoverspeed = 1022.7) is seen to perform better than Poisson regression (AICoverspeed = 7051.8, BICoverspeed = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.

scholarly journals Determinants of Transmission Risk During the Late Stage of the West African Ebola Epidemic

2019 ◽  
Vol 188 (7) ◽  
pp. 1319-1327
Author(s):  
Alexis Robert ◽  
W John Edmunds ◽  
Conall H Watson ◽  
Ana Maria Henao-Restrepo ◽  
Pierre-Stéphane Gsell ◽  
...  
Keyword(s):  
First Generation ◽  
Negative Binomial ◽  
Infected Individual ◽  
Negative Binomial Regression ◽  
West African ◽  
Transmission Risk ◽  
Treatment Unit ◽  
Data Set ◽  
Binomial Regression ◽  
Insight Into

Abstract Understanding risk factors for Ebola transmission is key for effective prediction and design of interventions. We used data on 860 cases in 129 chains of transmission from the latter half of the 2013–2016 Ebola epidemic in Guinea. Using negative binomial regression, we determined characteristics associated with the number of secondary cases resulting from each infected individual. We found that attending an Ebola treatment unit was associated with a 38% decrease in secondary cases (incidence rate ratio (IRR) = 0.62, 95% confidence interval (CI): 0.38, 0.99) among individuals that did not survive. Unsafe burial was associated with a higher number of secondary cases (IRR = 1.82, 95% CI: 1.10, 3.02). The average number of secondary cases was higher for the first generation of a transmission chain (mean = 1.77) compared with subsequent generations (mean = 0.70). Children were least likely to transmit (IRR = 0.35, 95% CI: 0.21, 0.57) compared with adults, whereas older adults were associated with higher numbers of secondary cases. Men were less likely to transmit than women (IRR = 0.71, 95% CI: 0.55, 0.93). This detailed surveillance data set provided an invaluable insight into transmission routes and risks. Our analysis highlights the key role that age, receiving treatment, and safe burial played in the spread of EVD.

scholarly journals Failing to Complain: Do Nursing Homes with more Residents with Dementia have Fewer Complaints?

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 847-848
Author(s):  
Kallol Kumar Bhattacharyya ◽  
Lindsay Peterson ◽  
John Bowblis ◽  
Kathryn Hyer
Keyword(s):  
Negative Binomial ◽  
Tracking System ◽  
Negative Binomial Regression ◽  
Policy Implications ◽  
Data Set ◽  
For Profit ◽  
Binomial Regression ◽  
Resource File ◽  
Area Health

Abstract The majority of nursing home (NH) residents have Alzheimer’s Disease or Related Dementias (ADRD). However, the association of ADRD prevalence and NH quality is unclear. The objective of the current study is to understand the association of NH characteristics, including the proportion of ADRD residents, with the prevalence of NH complaints as an indicator of quality of care and quality of life. We merged data from the ASPEN Complaints/Incident Tracking System with national NH data from the Certification and Survey Provider Enhanced Reports, the Minimum Data Set, the Area Health Resource File, and zip-code level rural-urban codes in 2017. Three groups of NHs were created, including those whose proportion of residents with ADRD was in the top decile (i.e., high-dementia NHs (N=1,473)) and those whose proportion of ADRD residents was in the lowest decile (i.e., low-dementia NHs (N=1,524)). Bivariate results revealed high-ADRD NHs had higher percentages of Medicaid-paying residents, were less likely to be for-profit and chain-affiliated, had lower staffing hours and lower percentages of Black, Hispanic, and Asian residents. Using NHs in the middle deciles as reference, negative binomial regression models showed that having a low proportion of ADRD residents was significantly associated with higher numbers of total complaints (p<.001) and substantiated complaints (p<.001), whereas having a high proportion of ADRD residents was significantly associated with lower numbers of substantiated complaints (p=.001). The findings suggest the proportion of residents with ADRD in NHs is associated with quality, as measured by complaints. Policy implications of these findings will be discussed.

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.

What Do We Know About Changing Economic Activity of Firms?

2020 ◽  
Vol 8 (2) ◽  
pp. 149-169
Author(s):  
Radhika Pandey ◽  
Amey Sapre ◽  
Pramod Sinha
Keyword(s):  
Economic Activity ◽  
Regression Models ◽  
Statistical Approach ◽  
Negative Binomial ◽  
Large Data ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
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

scholarly journals Analyzing Overdispersed Antenatal Care Visits of Pregnant Women in Bangladesh: Negative Binomial Regression Model

2017 ◽  
Vol 65 (2) ◽  
pp. 133-137
Author(s):  
Nasrin Sultana ◽  
Wasimul Bari
Keyword(s):  
Antenatal Care ◽  
Regression Model ◽  
Negative Binomial ◽  
Incidence Rate Ratio ◽  
Negative Binomial Regression ◽  
Demographic Variables ◽  
Negative Binomial Regression Model ◽  
Data Set ◽  
Care Visit ◽  
Binomial Regression

The main aim of this paper is to find out the potential determinants of antenatal care visits of women in Bangladesh during their pregnancy. The data set was extracted from Bangladesh Demographic and Health Survey, 2014 and overdispersion is found to be present in the data set. To take the overdispersion into account, negative binomial regression model has been used. A number of socioeconomic and demographic variables were observed to have significant impact on the antenatal care visit. Results are explained using incidence rate ratio. Dhaka Univ. J. Sci. 65(2): 133-137, 2017 (July)

scholarly journals Factors of the Prevalence of COVID-19 for Females in Amhara Region, Northern Ethiopia

2021 ◽  
pp. 13-18
Author(s):  
Yenew Alemu
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
P Value ◽  
Negative Binomial Regression Model ◽  
Northern Ethiopia ◽  
Amhara Region ◽  
Data Set ◽  
Global Epidemic ◽  
High Risk Factors ◽  
Binomial Regression

The COVID-19 pandemic in Ethiopia is a global epidemic of coronavirus disease 2019 caused by severe acute respiratory syndrome coronavirus 2. Amhara Region is a regional state in northern Ethiopia and the homeland of the Amhara people. The main objective of this study was to identify factors of the prevalence of COVID-19 for females in Amhara region. The data set was obtained from Amhara Public Health Institute 2020.  A negative Binomial regression model was conducted to find out the determinants of the prevalence of COVID-19 for females. Out of 5,627 confirmed cases, 96 patients have died and 1,483 confirmed cases were females from 138 daily reports.  Number of recovered COVID-19 cases ( = -0.4566332, 95%CI: (-0.7364772, -0.1767892), P-value < 0.05), severe cases ( = 1.038589, 95%CI: (0.7531619, 1.324017), P-value < 0.05), total deaths ( = 0.5164175, 95% CI: (0.1438362, 0.8889987), P-value <0.05) and the average age of patients per day ( = 1.511936, 95% C.I: (0.9220257, 2.101846), P-value < 0.05 ) were statistically significant factors for the confirmed case of COVID-19 for females in Amhara region. Above 30 average age of patients per day, below 19 number of recovered cases, above 12 number of severe cases, and above two number of deaths are optimally high-risk factors of confirmed cases of COVID-19 for females.

scholarly journals A Bayesian inference tool for identifying artifactual calls from differential transcript abundance analyses

2020 ◽  
Author(s):  
Stefano Mangiola ◽  
Evan A Thomas ◽  
Martin Modrák ◽  
Anthony T Papenfuss
Keyword(s):  
Negative Binomial ◽  
Genetic Material ◽  
Negative Binomial Regression ◽  
Transcript Abundance ◽  
Negative Binomial Regression Model ◽  
Data Set ◽  
Statistical Framework ◽  
Binomial Regression ◽  
Over Dispersion ◽  
Binomial Models

AbstractRelative transcript abundance has proven to be a valuable tool for inferring the phenotype of biological systems from genetic material. Several methods for the analysis of differential transcript abundance have been developed, and some of the most popular are based on negative binomial models. Although most genes are fitted reasonably well by the negative binomial distribution, the presence of outlier observations that do not fit such models can lead to artifactual identification of significant changes in transcription. Identifying those transcripts for the correct interpretation of results is extremely important. A robust and automated tool for detecting sample/transcript pairs that do not fit a negative binomial regression model is currently lacking. Here we propose ppcseq, a robust statistical framework that models hierarchically sample- and gene-wise features such as sequencing depth bias, the association between mean transcript abundance and its over-dispersion, and provides a theoretical transcript abundance distribution, on which the observed transcript abundance can be tested for outliers. We show using a publicly available data set where nearly 10% of differentially abundant transcripts had fold change inflated by the presence of outliers. This method has broad utility in filtering artifactual results of differential transcript abundance analyses based on a negative binomial framework.

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