International Journal of Statistics and Probability, Issue: Vol.9, No.3IJSPThu, 04 Jun 2020 07:37:44 +0000Zend_Feed_Writer 2 (http://framework.zend.com)
http://www.ccsenet.org/journal/index.php/ijsp
ijsp@ccsenet.org (International Journal of Statistics and Probability)International Journal of Statistics and ProbabilityA Paradox in Bland-Altman Analysis and a Bernoulli ApproachFri, 27 Mar 2020 10:08:41 +0000
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/42352
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/423520Comparing Weighted Markov Chain and Auto-Regressive Integrated Moving Average in the Prediction of Under-5 Mortality Annual Closing Rates in NigeriaIn developing countries, childhood mortality rates are not only affected by socioeconomic, demographic, and health variables, but also vary across regions. Correctly predicting childhood mortality rate trends can provide a clearer understanding for health policy formulation to reduce mortality.This paper describes and compares two prediction methods: Weighted Markov Chain Model (WMC) and Autoregressive Integrated Moving Average (ARIMA) in order to establish which method can better predict the annual child mortality rate in Nigeria. The data for the study were Childhood Mortality Annual Closing Rates (CMACR) data for Nigeria from 1964-2017. The CMACR provides random values changing over time (annually), so we can analyze the mortality closing rate and predict the change range in the next state. Weighted Markov Chain (WMC), a method based on Markov theory, addresses the state and its transition procedures to describe a changing random time series. While the Autoregressive Integrated Moving Average (ARIMA) is a generalization of an Autoregressive Moving Average (ARMA) model. The findings indicate that the ARIMA model predicts CMACR for Nigeria better than WMC. The WMC entered in a loop after two iterations, and we could not use it effectively to predict the future values of CMACR.]]>Fri, 27 Mar 2020 10:07:26 +0000
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/42354
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/423540The Impact of Women Parliamentarians on Economic Growth: Modelling & Statistical Analysis of Empirical Global DataThe impact of empowering women on economic growth is investigated through testing the influence of proportion of women candidates in parliaments. To obtain a long-term view, cross-country analysis is performed in parallel using 10 years World Bank data of 72 countries divided into the UN income-groups: 1-high, 2-upper-middle, 3-lower-middle, and 4- low-income. Statistical analysis reveals severe degree of multicollinearity. To unveil the desired connection, two approaches are implemented. Principle Component Regression is used to assess the independent impact of women parliamentarians. The results demonstrate a positive significant influence: 10% increase in female parliamentarians increase growth by 0.27%, 0.36%, 0.22%, and 0.49%, respectively. To unveil the joint influence of the considered indicators, interaction regression models are developed. The method demonstrates superior results. This work provides an empirical evidence on the positive impact of women political empowerment with respect to stimulating a sustainable economic growth.]]>Wed, 29 Apr 2020 07:50:57 +0000
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/42498
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/424980Characterizations of Exponentiated Generalized Power Lindley and Geometric-Zero Truncated Poisson DistributionsMirMostafaee et al. (2019) proposed a continuous univariate distribution called Exponentiated Generalized Power Lindley (EGPL) distribution and studied certain properties and applications of their distribution. Akdogan et al. (2019) introduced a discrete distribution called Geometric-Zero Truncated Poisson (GZTP) distribution and provided its properties and applications. The present short note is intended to complete, in some way, the works cited above via establishing certain characterizations of the EGPL and GZTP distributions in different directions.]]>Mon, 13 Apr 2020 06:41:52 +0000
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/42512
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/425120Gaussian Model to Predict the Risk of Developing Type 2 Diabetes Mellitus in Mexican Population Taking as a Reference Risk FactorsIn this research work, an ordinal Gaussian model is constructed, whose objective is to predict the degree of risk of contracting type 2 diabetes mellitus (2DM), taking as reference the risk factors in the Mexican population. It is estimated that the Mexican population has a hereditary susceptibility to develop 2DM, however, the probability increases depending on risk factors; area of residence, background of parents with 2DM, tobacco consumption, alcohol consumption, physical inactivity, body mass index (BMI), and type of feeding, which, despite positively intervening in the appearance of 2DM, they can be modified to obtain the inversely proportional effect. However, the social, economic and political context are important components for the population. Risk factors, as explanatory elements of the prevalence of 2DM, are of the utmost importance to delay or control their early development, as some are factors that can be muffled. For the development of this model, the information published in the National Health and Nutrition Survey (ENSANUT) of 2012 was taken, based on the adult population 20 years of age or older. Among the most outstanding results is the higher prevalence of risk that women have with respect to men, and the fact that age is a fundamental basis for contracting type 2 diabetes mellitus.]]>Wed, 29 Apr 2020 08:05:48 +0000
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/42529
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/425290A Comparison of a General Linear Model and the Ratio EstimatorIn data analysis, selecting a proper statistical model is a challenging issue. Upon the selection, there are other important factors impacting the results. In this article, two statistical models, a General Linear Model (GLM) and the Ratio Estimator will be compared. Where applicable, some issues such as heteroscedasticity, outliers, etc. and the role they play in data analysis will be studied. For reducing the severity of heteroscedasticity, Weighted Least Square (WLS), Generalized Least Square (GLS), and Feasible Generalized Least Square (FGLS) will be deployed. Also, a revised version of FGLS is introduced. Since these issues are data dependent, shrimp effort data collected in the Gulf of Mexico for the years 2005 through 2018 will be used and it is shown that the revised FGLS reduces the impact of heteroscedasticity significantly compared to that of FGLS. The data sets will also be checked for the outliers and corrections are made (where applicable). It is concluded that these issues play a significant role in data analysis and must be taken seriously. Further, the two statistical models, that is, the GLM and the Ratio Estimator are compared.]]>Wed, 29 Apr 2020 08:18:14 +0000
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/42530
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/425300Combined Nearest Greedy Algorithm With Randomized Iterated Greedy Algorithm to Solve Waste Collection ProblemThe waste collection considers as one of the common transportation problems in the operation research and management area and also it is a significant activity in each city. In this kind of the paper, we study a version of a real-life waste collection and try to find an efficient way to reduce the costs such as the cost of the operation such as fuel and maintenances, the cost of the environment such as noise and traffic congestions, the cost of the investment such as vehicles fleet. The waste collection problem can be formulated as a well-known Vehicle Routing Problem (VRP). The basic idea is to attempt to develop a daily truck routing which will improve the efficiency of the vehicle distribution in Riyadh. The solution will be done in a good way that it can serve all the customers, while in the meantime, it will attempt to improve the total cost. The main contribution of this paper is to improve the solution of total costs for the waste collection while using the existing resources through the combination of the Nearest Greedy algorithm with both Iterated Greedy (IG) and Randomized Iterated Greedy (RIG). We execute our proposed method with real data that collect waste from more than 100 customers in Riyadh city. In terms of the experiments, the results received by those methods are successfully implemented and improved the overall waste collection in Riyadh. In conclusion, these algorithms able to reduce the total costs to this kind of case study with the same number of vehicles. ]]>Wed, 29 Apr 2020 06:19:15 +0000
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/42581
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/425810Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 9, No. 3Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 9, No. 3, 2020]]>Wed, 29 Apr 2020 06:34:33 +0000
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/42618
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/426180