scholarly journals Civil Aviation Occurrences in Slovakia and their Evaluation Using Statistical Methods

2021 ◽  
Vol 13 (10) ◽  
pp. 5396
Author(s):  
Miriam Andrejiova ◽  
Anna Grincova ◽  
Daniela Marasova ◽  
Peter Koščák
Keyword(s):  
Poisson Regression ◽  
Information Criterion ◽  
Civil Aviation ◽  
Poisson Regression Model ◽  
Expected Number ◽  
Aviation Accidents ◽  
Pareto Analysis ◽  
Input Variables ◽  
Commercial Aircrafts ◽  
Technical Failures

The nature of a civil aviation occurrence may be defined in three different categories while considering its severity. General categories include civil aviation accidents, serious incidents and incidents. The present article analyses the civil aviation occurrences in Slovakia which happened in the period from 2000 to 2019. In this period, there was a significant increase in the number of civil aviation occurrences, and incidents, in particular, represented the highest percentage. A Pareto analysis was applied to identify the key incident categories (wildlife strike, technical failures of the aviation technology and unauthorised penetration of airspace). A multiple regression analysis and the Poisson regression were used to create two models of correlations between the number of civil aviation occurrences and the selected input variables. Both models are statistically significant, and, based on the AIC (Akaike Information Criterion), the Poisson regression model appeared to be of higher quality. The model showed, for example, that an increase in variables (the number of commercial aircrafts aged over 14 years and the number of total aircraft movements) resulted in a slight increase in the expected number of civil aviation occurrences.

scholarly journals Impact of COVID-19 on mortality in Croatia

Stanovnistvo ◽  
2021 ◽  
Vol 59 (1) ◽  
pp. 1-16
Author(s):  
Ivan Cipin ◽  
Dario Mustac ◽  
Petra Medjimurec
Keyword(s):  
Life Expectancy ◽  
Poisson Regression ◽  
Excess Mortality ◽  
Age Groups ◽  
Poisson Regression Model ◽  
Decomposition Techniques ◽  
Expected Number ◽  
The Difference ◽  
Age And Sex ◽  
Related Mortality

The main goal of this paper is to assess the effects of the COVID-19 pandemic on mortality in Croatia. We estimate two effects of the pandemic on mortality: (1) excess mortality during 2020 and (2) the age- and cause-specific components of life expectancy decline in 2020. We calculate excess mortality in 2020 as the difference between the registered number of deaths in 2020 and the expected number of deaths from a Poisson regression model based on weekly death counts and population exposures by age and sex from 2016 to 2019. Using decomposition techniques, we estimate age- and cause-specific components (distinguishing COVID-19-related deaths from deaths from other causes) of life expectancy decline in 2020. Our results show that excess mortality in 2020 almost entirely results from the second, autumn-winter wave of the epidemic in Croatia. Expectedly, we find the highest excess in deaths in older age groups. In Croatia, life expectancy in 2020 fell by almost eight months for men and about seven months for women. This decline is mostly attributable to COVID-19-related mortality in older ages, especially among men.

scholarly journals The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huihui Zhang ◽  
Yini Liu ◽  
Fangyao Chen ◽  
Baibing Mi ◽  
Lingxia Zeng ◽  
...  
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Linear Models ◽  
Intervention Strategy ◽  
Health Resources ◽  
Sociodemographic Factors ◽  
Information Criteria ◽  
Poisson Regression Model ◽  
Geographically Weighted Regression Model ◽  
Auto Correlation

Abstract Background Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective. Methods Official surveillance data about the COVID-19 and sociodemographic information in China’s 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis. Results Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran’s I = − 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased. Conclusions There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.

A multivariate Poisson regression model for count data

2021 ◽  
pp. 1-17
Author(s):  
J. M. Muñoz-Pichardo ◽  
R. Pino-Mejías ◽  
J. García-Heras ◽  
F. Ruiz-Muñoz ◽  
M. Luz González-Regalado
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Poisson Regression Model

Monitoring social networks based on Zero-inflated Poisson regression model

2021 ◽  
pp. 1-17
Author(s):  
Narges Motalebi ◽  
Mohammad Saleh Owlia ◽  
Amirhossein Amiri ◽  
Mohammad Saber Fallahnezhad
Keyword(s):  
Social Networks ◽  
Regression Model ◽  
Poisson Regression ◽  
Poisson Regression Model

scholarly journals Age and Sex Specific Trends in Incidence of Juvenile Idiopathic Arthritis in Danish Birth Cohorts from 1992 to 2002: A Nationwide Register Linkage Study

2021 ◽  
Vol 18 (16) ◽  
pp. 8331
Author(s):  
Isabel Cardoso ◽  
Peder Frederiksen ◽  
Ina Olmer Specht ◽  
Mina Nicole Händel ◽  
Fanney Thorsteinsdottir ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Regression Model ◽  
Incidence Rate ◽  
Poisson Regression ◽  
Linkage Study ◽  
Incidence Rates ◽  
Age Group ◽  
Poisson Regression Model ◽  
Birth Cohorts ◽  
Age And Sex

This study reports age- and sex-specific incidence rates of juvenile idiopathic arthritis (JIA) in complete Danish birth cohorts from 1992 through 2002. Data were obtained from the Danish registries. All persons born in Denmark, from 1992–2002, were followed from birth and until either the date of first diagnosis recording, death, emigration, 16th birthday or administrative censoring (17 May 2017), whichever came first. The number of incident JIA cases and its incidence rate (per 100,000 person-years) were calculated within sex and age group for each of the birth cohorts. A multiplicative Poisson regression model was used to analyze the variation in the incidence rates by age and year of birth for boys and girls separately. The overall incidence of JIA was 24.1 (23.6–24.5) per 100,000 person-years. The rate per 100,000 person-years was higher among girls (29.9 (29.2–30.7)) than among boys (18.5 (18.0–19.1)). There were no evident peaks for any age group at diagnosis for boys but for girls two small peaks appeared at ages 0–5 years and 12–15 years. This study showed that the incidence rates of JIA in Denmark were higher for girls than for boys and remained stable over the observed period for both sexes.

PARAMETER ESTIMATION ON HURDLE POISSON REGRESSION MODEL WITH CENSORED DATA

2012 ◽  
Vol 57 (1) ◽  
Author(s):  
SEYED EHSAN SAFFAR ◽  
ROBIAH ADNAN ◽  
WILLIAM GREENE
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Goodness Of Fit ◽  
Poisson Model ◽  
Likelihood Method ◽  
Poisson Regression Model ◽  
Response Variable ◽  
The Mean ◽  
Over Dispersion

A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the same as before. In fact, the variance value of the dependent variable will be much more than the mean value of the dependent variable and this is called over–dispersion. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, it is suggested to use a hurdle Poisson regression model to overcome over–dispersion problem. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle Poisson regression model is introduced on count data with many zeros. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness–of–fit for the regression model is examined. We study the effects of right censoring on estimated parameters and their standard errors via an example.

Using Poisson Regression Model to Examine Student Travel Frequency Patterns in Beijing

2014 ◽  
Vol 1030-1032 ◽  
pp. 2738-2741
Author(s):  
Guang Jun Zhan
Keyword(s):  
University Students ◽  
Regression Model ◽  
Poisson Regression ◽  
Family Income ◽  
Influence Factors ◽  
Poisson Regression Model ◽  
Web Based ◽  
Study Results ◽  
Online Travel ◽  
Student Travel

This paper applies Poisson regression model to examine university students' travel frequencies and relevant influence factors, using the data collected from four universities in Beijing by a web-based online travel survey. It finds that student grade, family income and school attended have significant effects on students' travel frequency. The study results reveal students travel frequency characteristics at a disaggregate level and provide information to well understand student travel frequency patterns.

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