The beta and simplex regression models to explain homicides in state capitals of Brazil

2020 ◽  
Vol 15 (3) ◽  
pp. 215-224
Author(s):  
Gauss M. Cordeiro ◽  
Enivaldo Rocha ◽  
Dalson Figueiredo ◽  
Antônio Fernandes ◽  
Edwin M.M. Ortega ◽  
...  
Keyword(s):  
Gini Coefficient ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Negative Impact ◽  
R Package ◽  
Developed Countries ◽  
Dispersion Parameters ◽  
Homicide Rates ◽  
Murder Rate ◽  
The Mean

The intentional killing of one human being by its own kind is considered the worst of the crimes. Therefore, homicide prevention is a major concern for policy makers in both developing and developed countries. We propose regression modeling for the homicide rates in Brazil along with appropriately chosen distributions for these responses that are in agreement with the restriction of values to the unit interval. We adopt the beta and simplex regression models with systematic components for the mean and dispersion parameters to explain the homicide rates in 27 state capitals of Brazil from the following explanatory variables: time, Gini coefficient, municipal human development index (MHDI), illiteracy and poverty rates. We employ standard likelihood techniques, perform influence and residual analysis and calculate goodness-of-fit statistics to select the best regression to explain homicides rates in these capitals. We perform the computations in the R package. The main results suggest the following: the mean homicide rate is increasing over time; there is a negative correlation between MHDI and murder rate; the poverty has a quite small negative impact on the mean homicide rates in the beta regression. The Gini coefficient and the illiteracy and poverty rates explain the dispersion of the homicide rates.

Flexible quasi-beta regression models for continuous bounded data

2018 ◽  
Vol 19 (6) ◽  
pp. 617-633 ◽  
Author(s):  
Wagner H Bonat ◽  
Ricardo R Petterle ◽  
John Hinde ◽  
Clarice GB Demétrio
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Beta Regression ◽  
Estimating Function ◽  
Dispersion Parameters ◽  
Linear Predictor ◽  
Mean And Variance ◽  
The Mean ◽  
Bounded Data ◽  
Beta Regression Model

We propose a flexible class of regression models for continuous bounded data based on second-moment assumptions. The mean structure is modelled by means of a link function and a linear predictor, while the mean and variance relationship has the form [Formula: see text], where [Formula: see text], [Formula: see text] and [Formula: see text] are the mean, dispersion and power parameters respectively. The models are fitted by using an estimating function approach where the quasi-score and Pearson estimating functions are employed for the estimation of the regression and dispersion parameters respectively. The flexible quasi-beta regression model can automatically adapt to the underlying bounded data distribution by the estimation of the power parameter. Furthermore, the model can easily handle data with exact zeroes and ones in a unified way and has the Bernoulli mean and variance relationship as a limiting case. The computational implementation of the proposed model is fast, relying on a simple Newton scoring algorithm. Simulation studies, using datasets generated from simplex and beta regression models show that the estimating function estimators are unbiased and consistent for the regression coefficients. We illustrate the flexibility of the quasi-beta regression model to deal with bounded data with two examples. We provide an R implementation and the datasets as supplementary materials.

scholarly journals Impacts from Economic Development and Environmental Factors on Life Expectancy: A Comparative Study Based on Data from Both Developed and Developing Countries from 2004 to 2016

2021 ◽  
Vol 18 (16) ◽  
pp. 8559
Author(s):  
Zhiheng Chen ◽  
Yuting Ma ◽  
Junyi Hua ◽  
Yuanhong Wang ◽  
Hongpeng Guo
Keyword(s):  
Developing Countries ◽  
Economic Development ◽  
Gini Coefficient ◽  
Negative Impact ◽  
Positive Impact ◽  
Developed Countries ◽  
Environmental Indicators ◽  
Gdp Per Capita ◽  
Per Capita ◽  
Urbanization Rate

Both economic development level and environmental factors have significant impacts on life expectancy at birth (LE). This paper takes LE as the research object and selects nine economic and environmental indicators with various impacts on LE. Based on a dataset of economic and environmental indicators of 20 countries from 2004 to 2016, our research uses the Pearson Correlation Coefficient to evaluate the correlation coefficients between the indicators, and we use multiple regression models to measure the impact of each indicator on LE. Based on the results from models and calculations, this study conducts a comparative analysis of the influencing mechanisms of different indicators on LE in both developed and developing countries, with conclusions as follow: (1) GDP per capita and the percentage of forest area to land area have a positive impact on LE in developed countries; however, they have a negative impact on LE in developing countries. Total public expenditure on education as a percentage of GDP and fertilizer consumption have a negative impact on LE in developed countries; however, they have a positive impact on LE in developing countries. Gini coefficient and average annual exposure to PM2.5 have no significant effect on LE in developed countries; however, they have a negative impact on LE in developing countries. Current healthcare expenditures per capita have a negative impact on LE in developed countries, and there is no significant impact on LE in developing countries. (2) The urbanization rate has a significant positive impact on LE in both developed countries and developing countries. Carbon dioxide emissions have a negative impact on LE in both developed and developing countries. (3) In developed countries, GDP per capita has the greatest positive impact on LE, while fertilizer consumption has the greatest negative impact on LE. In developing countries, the urbanization rate has the greatest positive impact on LE, while the Gini coefficient has the greatest negative impact on LE. To improve and prolong LE, it is suggested that countries should prioritize increasing GDP per capita and urbanization level. At the same time, countries should also work on reducing the Gini coefficient and formulating appropriate healthcare and education policies. On the other hand, countries should balance between economic development and environmental protection, putting the emphasis more on environmental protection, reducing environmental pollution, and improving the environment’s ability of self-purification.

scholarly journals Goftte: A R package for assessing goodness-of-fit in proportional (sub) distributions hazards regression models

2019 ◽  
Vol 177 ◽  
pp. 269-275 ◽  
Author(s):  
P. Sfumato ◽  
T. Filleron ◽  
R. Giorgi ◽  
R.J. Cook ◽  
J.M. Boher
Keyword(s):  
Regression Models ◽  
Goodness Of Fit ◽  
R Package

Impact of Governance on FDI: Evidence from Dynamic Panel Regression Models

2020 ◽  
Vol 66 (2) ◽  
pp. 131-144
Author(s):  
Fabian Reck
Keyword(s):  
Developing Countries ◽  
Regression Models ◽  
Developed Countries ◽  
Institutional Setting ◽  
Dynamic Panel ◽  
Governance Indicators ◽  
Country Characteristics ◽  
Dynamic Panel Regression ◽  
The Mean ◽  
World Governance Indicators

This paper analyzes the effect of world governance indicators on inward foreign direct investment. The sample covers 38 developed and 82 developing countries between 2002 and 2018. The results of the system GMM regressions suggest that governance indicators are important determinants of inward FDI for developed countries. Moreover, it is shown that it is important to control for dynamic effects. In comparison, for developing countries, other country characteristics – the mean tariff rate – are more important than the institutional setting.

Bayesian beta regression models with joint mean and dispersion modeling

2015 ◽  
Vol 21 (1) ◽  
Author(s):  
Edilberto Cepeda-Cuervo ◽  
Liliana Garrido
Keyword(s):  
Regression Models ◽  
Bayesian Method ◽  
Joint Modeling ◽  
Dispersion Modeling ◽  
Beta Regression ◽  
Dispersion Parameters ◽  
The Mean

AbstractThis paper summarizes some results of beta regression models and proposes a Bayesian method to fit these models, including joint modeling of the mean and dispersion parameters. This method is implemented through simulated and applied studies.

New Drugs for Thromboembolic Prevention in Atrial Fibrillation

2010 ◽  
Vol 6 (4) ◽  
pp. 64
Author(s):  
Jose L Merino ◽  
Jose López-Sendón ◽  
◽  
Keyword(s):  
Atrial Fibrillation ◽  
Vitamin K ◽  
Negative Impact ◽  
Vitamin K Antagonists ◽  
Coagulation Factor ◽  
Developed Countries ◽  
New Drugs ◽  
Risk Scores ◽  
Bleeding Events ◽  
Risk Patients

Atrial fibrillation (AF) is the most frequent sustained arrhythmia and its prevalence is increasing in developed countries. This progressive increase and the negative impact of this arrhythmia on the patient’s prognosis make AF one of the main healthcare problems faced today. This has led to intense research into the main aspects of AF, one of them being thromboembolism prevention. AF patients have a four to five times higher risk of stroke than the general population. Several factors increase thromboembolic risk in patients with AF and the use of risk scores, such as the Congestive Heart Failure, Hypertension, Age Greater than 75, Diabetes, and Prior Stroke or Transient Ischemic Attack (CHADS2), have been used to identify the best candidates for anticoagulation. Antithrombotic drugs are the mainstay of therapy for embolic prevention. The clinical use of these drugs is based on the risk–benefit ratio, where benefit is the reduction of stroke and systemic embolic events and risk is mostly driven by the increase in bleeding events. Generally, antiplatelets are indicated for low-risk patients in light of the fact anticoagulants are the drug of choice for moderate- or high-risk patients. Vitamin K antagonists have been the only option for oral anticoagulation for the last 50 years. However, these drugs have many pharmacodynamic and pharmacokinetic problems. The problems of anticoagulation with vitamin K antagonists have led to the investigation of new drugs that can be administered orally and have a better dose–response relationship, a shorter half-life and, in particular, higher efficacy and safety without the need for frequent anticoagulation controls. The drugs that have been studied most thoroughly in patients with AF are inhibitors of the activated coagulation factor X and inhibitors of coagulation factor II (thrombin), including ximelagatran and dabigatran. In addition, non-pharmacological therapies have been developed to prevent recurrent embolism in certain patient populations.

European and Ukrainian experience in financial support to farming activities through fiscal instruments

2020 ◽  
Vol 23 (9) ◽  
pp. 1040-1063
Author(s):  
E.A. Nepochatenko ◽  
E.T. Prokopchuk ◽  
B.S. Guzar
Keyword(s):  
Agricultural Production ◽  
Financial Incentives ◽  
Financial Support ◽  
Financial Regulation ◽  
Negative Impact ◽  
Developed Countries ◽  
European Countries ◽  
Main Task ◽  
Statistical Research ◽  
Tax Regimes

Subject. The article considers financial regulation through the use of tax mechanisms. Objectives. The aim of the study is to evaluate European and Ukrainian practices of fiscal incentives for farming through fiscal instruments with VAT playing the key role. Methods. In the study we employed economic and statistical research methods, like monographic, comparison, scientific generalization. Results. Based on the analysis of VAT implementation on farmers in developed countries in Europe we substantiated the conclusion about its focus on simplifying the tax procedures and eliminating the negative impact on operations of economic entities. Special tax treatment (including VAT collection) is mainly used to streamline tax relations, taking into account the specifics of farming, rather than to improve the financial support to farms. We revealed that in the Ukrainian practice its main task is financial support to agricultural production. Conclusions and Relevance. The experience of developed European countries on the use of special tax regimes and taxation procedures should serve as a model for Ukraine. Financial incentives for agricultural production development should be directly supported by the State, and special tax treatment and tax administration should be focused on streamlining tax relations in the region, based on the practice of developed European countries such as UK, Germany, Italy and France.

Assessing single nucleotide polymorphism selection methods for the development of a low-density panel optimized for imputation in South African Drakensberger beef cattle

2021 ◽  
Author(s):  
Simon F Lashmar ◽  
Donagh P Berry ◽  
Rian Pierneef ◽  
Farai C Muchadeyi ◽  
Carina Visser
Keyword(s):  
South African ◽  
Clustering Algorithm ◽  
Imputation Accuracy ◽  
Developed Countries ◽  
Genotype Imputation ◽  
Selection Strategy ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Single Nucleotide Polymorphism Selection ◽  
The Mean

Abstract A major obstacle in applying genomic selection (GS) to uniquely adapted local breeds in less-developed countries has been the cost of genotyping at high densities of single nucleotide polymorphisms (SNP). Cost reduction can be achieved by imputing genotypes from lower to higher densities. Locally adapted breeds tend to be admixed and exhibit a high degree of genomic heterogeneity thus necessitating the optimization of SNP selection for downstream imputation. The aim of this study was to quantify the achievable imputation accuracy for a sample of 1,135 South African (SA) Drakensberger using several custom-derived lower-density panels varying in both SNP density and how the SNP were selected. From a pool of 120,608 genotyped SNP, subsets of SNP were chosen 1) at random, 2) with even genomic dispersion, 3) by maximizing the mean minor allele frequency (MAF), 4) using a combined score of MAF and linkage disequilibrium (LD), 5) using a partitioning-around-medoids (PAM) algorithm, and finally 6) using a hierarchical LD-based clustering algorithm. Imputation accuracy to higher density improved as SNP density increased; animal-wise imputation accuracy defined as the within-animal correlation between the imputed and actual alleles ranged from 0.625 to 0.990 when 2,500 randomly selected SNP were chosen versus a range of 0.918 to 0.999 when 50,000 randomly selected SNP were used. At a panel density of 10,000 SNP, the mean (standard deviation) animal-wise allele concordance rate was 0.976 (0.018) versus 0.982 (0.014) when the worst (i.e., random) as opposed to the best (i.e., combination of MAF and LD) SNP selection strategy was employed. A difference of 0.071 units was observed between the mean correlation-based accuracy of imputed SNP categorized as low (0.01<MAF≤0.1) versus high MAF (0.4<MAF≤0.5). Greater mean imputation accuracy was achieved for SNP located on autosomal extremes when these regions were populated with more SNP. The presented results suggested that genotype imputation can be a practical cost-saving strategy for indigenous breeds such as the South African Drakensberger. Based on the results, a genotyping panel consisting of approximately 10,000 SNP selected based on a combination of MAF and LD would suffice in achieving a less than 3% imputation error rate for a breed characterized by genomic admixture on the condition that these SNP are selected based on breed-specific selection criteria.

scholarly journals Correction to: MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Matthew D. Koslovsky ◽  
Marina Vannucci
Keyword(s):  
Variable Selection ◽  
Regression Models ◽  
R Package ◽  
Bayesian Variable Selection ◽  
Multinomial Regression

An amendment to this paper has been published and can be accessed via the original article.

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