scholarly journals E-optimal Experimental Designs for Poisson Regression Models in Two and Three Variables

2021 ◽  
Vol 47 (3) ◽  
pp. 999-1006
Author(s):  
Emmanuel I Olamide ◽  
Olusoga A Fasoranbaku ◽  
Femi B Adebola
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Prediction Error ◽  
Regression Models ◽  
Information Matrix ◽  
Error Variance ◽  
Experimental Designs ◽  
Poisson Regression Model ◽  
Prediction Error Variance ◽  
Design Weights

In the context of generalized linear models, most of the recent studies were on logistic regression models and many of them focussed on optimal experimental designs with concentration on D-optimality. In this research, two- and three-variable Poisson regression models were considered for E-optimization on restricted design space [0, 1]. The two-variable Poisson regression model was not optimal at 3-design points, but was found to be E-optimal at 4-design points (1, 1), (0, 0), (0, 1) and (1, 0) with equal design weights of 0.25. The three-variable Poisson regression model was E-optimal at 4-design points (0, 0, 1), (0, 1, 0), (1, 1, 1) and (1, 0, 0) with each design point having design weights of 0.25. The prediction error variance (PEV) for the two-variable Poisson regression model is 0.35 and that of the three-variable Poisson regression model is 0.68. From this research, the two-variable Poisson regression model is preferred to the three-variable Poisson regression model because of smaller PEV. Keywords: E-optimality; Fisher Information Matrix; Poisson Regression Model; Prediction Error Variance

scholarly journals Determinants of Under-Five Mortality in Tach-Armachiho District, North Gondar, Ethiopia

2021 ◽  
Author(s):  
AGMAS SISAY ABERA ◽  
HUNACHEW KIBRET YOHANNIS
Keyword(s):  
Prenatal Care ◽  
Regression Model ◽  
Birth Interval ◽  
Random Sampling ◽  
Poisson Regression ◽  
Regression Models ◽  
Negative Binomial ◽  
Poisson Regression Model ◽  
Place Of Delivery ◽  
Under Five

Abstract Background: Under-five mortality rate, often known by its acronym U5MR, indicates the probability of dying between birth and five years of age, expressed per 1,000 live births. Globally, 16,000 children under-five still die every day. Especially in Sub-Saharan Africa every 1 child in 12, dying before his or her fifth birthday. This study aims to identify the determinants of under-five mortality among women in child bearing age group of Tach-Armachiho district using count regression models. Methods: For achieving the objective, a two stage random sampling technique (simple random sampling and systematic random sampling techniques in the first and second stages respectively) was used to select women respondents. The sample survey conducted in Tach-Armachiho district considered a total of 3815 households of women aged 15 to 49 years out of which the information was collected from 446 selected women through interviewer administrated questionnaire. Results: The descriptive statistics result showed that in the district 16.6% of mothers have faced the problem of at least one under-five death. In this study, Poisson regression, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models were applied for data analysis. Each of these count models were compared by different statistical tests. So that, zero-inflated poisson regression model was found to be the best fit for the collected data. Results of the zero-inflated Poisson regression model showed that education of husband, source of water, mother occupation, kebele of mother, prenatal care, place of delivery, place of residence, wealth of house hold, average birth interval and average breast feeding were found to be statistically significant determinants of under-five mortality. Conclusions: In this study, it was found that the factors like average birth interval and average breast feeding were found to be statistically significant factors in both groups (not always zero category and always zero category) with under-five child death whereas education of husband, source of water, place of delivery, mother occupation and wealth index of the household have significant effect on under-five mortality under not always zero group. Place of residence, kebele of mother and prenatal care have a significant effect on under-five mortality in Tach-Armachiho district on inflated group.

scholarly journals Weighted Statistical A-optimal Designs for Two-variable Poisson Regression Model with Application to Fertility Studies

2021 ◽  
Vol 47 (3) ◽  
pp. 1303-1309
Author(s):  
Emmanuel Idowu Olamide ◽  
Femi Barnabas Adebola ◽  
Olusoga Akin Fasoranbaku
Keyword(s):  
Optimal Design ◽  
Regression Model ◽  
Primary Education ◽  
Poisson Regression ◽  
Information Matrix ◽  
Imperialist Competitive Algorithm ◽  
Optimal Designs ◽  
Poisson Regression Model ◽  
Marriage Duration ◽  
General Equivalence Theorem

This research extends design optimization to model involving count data. A two-variable Poisson regression model was investigated for A-optimality on a constrained design space and the weights of the optimal design points were obtained. The constructed designs were verified to be A-optimal at 4-point design through the general equivalence theorem. The efficiency of the constructed optimal design was found to be 100% A-efficient. The concept of weighted optimal designs for Poisson regression model was applied to fertility studies. Approximate A-optimal design weights of educational level of women were obtained for each marriage duration period with respect to their places of residence. The study revealed that the numbers of women with secondary education and above were found to be consistently more than that of women with no education, lower primary education and upper primary education, respectively for all the marriage duration periods considered and at each place of residence. The only exclusion is the marriage duration of 0–4 years at Suva where the proportion of women with no education was more than other educational levels. Keywords:    A-optimality; Design Point; Fisher Information Matrix; Imperialist Competitive Algorithm; Poisson Regression Model

scholarly journals Prediction of the Effect of Demographic Characteristics on Parity Using Poisson Regression Model

2020 ◽  
pp. 55-63
Author(s):  
C. M. Gatwiri ◽  
M. M. Muraya ◽  
L. K. Gitonga
Keyword(s):  
Regression Model ◽  
Marital Status ◽  
Poisson Regression ◽  
Regression Models ◽  
Poisson Model ◽  
Demographic Characteristics ◽  
Ratio Test ◽  
Poisson Regression Model ◽  
Individual Character ◽  
Dispersion Test

There is growing interest among the public in demography since demographic change has become the subject of political debates in many countries. Statistics on demography are used to support policy-making and monitor demographic behaviour of political, economic, social and cultural perspectives. Most studies have used descriptive statistics to study demographic characteristics. Moreover, most of these studies investigate effects of individual character at a time. Therefore, there is a need to come up with more robust statistical methods, such as predictive models for demographic studies. The objective of this study was to predict the effect of demographic characteristics on parity using Poisson regression model. Secondary data on parity, age, marital status and education level was collected from Chuka and Embu hospital maternal units from 2013 to 2017. The data was analysed using R-statistical software. Three Poisson regression models (PRMs) were fitted. The likelihood ratio test of all the Poisson regression models had p-values < 0.05 indicating that all the models were statistically significant. Deviance test and Akaike Information Criterion (AIC) were used to assess the fit of Poisson regression models. The overall Poisson model had residual deviance of 184.23, which was the lowest of all other fitted PRM models, suggesting that it was the best fit. The AIC of the PRM with both education and marital status as the predictors had the lowest AIC value of 2078.620, indicating that it was the best fitted model. The dispersion test proved that PRM was not over-dispersed, confirming the model as a good fit of the data. The improved model can be used in prediction of population growth rates.

scholarly journals Comparison between Efficiency of Poisson Regression Model and Negative Binomial Regression in the Analysis of Factors Affecting Mortality from Cardiovascular Diseases in Yazd Province in 2017

2021 ◽  
Author(s):  
Mohammad Mirjani Arjenan ◽  
Mohsen Askarshahi ◽  
Mahmud Vakili
Keyword(s):  
Cardiovascular Diseases ◽  
Regression Model ◽  
Poisson Regression ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Poisson Regression Model ◽  
Registration System ◽  
Factors Affecting ◽  
Binomial Regression

Introduction: Despite the advances in cardiovascular diseases, death caused by these diseases is still considered as the leading cause of mortality. In this study, some of the effective factors on the deaths caused by cardiovascular diseases were investigated Methods: This cross-sectional analytical study investigated the efficacy of Poisson regression models and negative binomial regression models on factors affecting mortality from cardiovascular diseases. The death data were extracted from the death registration system for Yazd province in 2017.Gender, age, education, occupation, location, and city of death were also extracted for each deceased. The two regression models were then fitted to the data Results:  A total of 5,015 deaths were recorded, of which 1,642 were due to cardiovascular diseases. Cardiovascular disease mortality rates were significant using negative binomial regression in terms of the educational variables, place of residence, type of residence, and age. Death rates caused by cardiovascular diseases were not significant for age and occupational, educational, and residential variables. Conclusion: If the time of death is considered as an offset variable, the regression model of two negative sentences is more effective in showing the factors affecting death due to cardiovascular diseases according to AIC and BIC criteria. In the case that the total number of deaths is considered as the offset variable, the Poisson regression model is more efficient.

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.

scholarly journals Asymptotic Prediction Error Variance for Feedforward Neural Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 1108-1113
Author(s):  
Magnus Malmström ◽  
Isaac Skog ◽  
Daniel Axehill ◽  
Fredrik Gustafsson
Keyword(s):  
Neural Networks ◽  
Prediction Error ◽  
Feedforward Neural Networks ◽  
Error Variance ◽  
Prediction Error Variance

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.

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