Estimating Weibull Parameters Using Least Squares and Multilayer Perceptron vs. Bayes Estimation

The Weibull distribution is regarded as among the finest in the family of failure distributions.One of the most commonly used parameters of the Weibull distribution(WD)is the ordinary least squares(OLS)technique,which is useful in reliability and lifetime modeling.In this study,we propose an approach based on the ordinary least squares and the multilayer perceptron(MLP)neural network called the OLSMLP that is based on the resilience of the OLS method.The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers,and eases the difficulty of determining weights in case of the weighted least square(WLS).Another method is proposed by incorporating a weight into the general entropy(GE)loss function to estimate the parameters of the WD to obtain a modified loss function(WGE).Furthermore,a Monte Carlo simulation is performed to examine the performance of the proposed OLSMLP method in comparison with approximate Bayesian estimation(BLWGE)by using a weighted GE loss function.The results of the simulation showed that the two proposed methods produced good estimates even for small sample sizes.In addition,the techniques proposed here are typically the preferred options when estimating parameters compared with other available methods,in terms of the mean squared error and requirements related to time.

The Effect of Foreign Direct Investment on Air Pollution in the Economic Community of West African States region: What Influence Does Tax Expenditure Have?

Air pollution is one of the crucial environmental challenges facing the countries of the Economic Community of West African States (ECOWAS). The objective of this paper is to examine the effect of an attractive tax policy on the relationship between Foreign Direct Investment (FDI) and air pollution in ECOWAS region over the period 2000 to 2019. By using the Ordinary Least Squares (OLS) method and panel data analyses (fixed effects and random effects), the results show that, in general, FDI does not have a significant effect on air pollution in the region. However, closer analysis reveals that an interaction between FDI and an attractive tax policy has a negative effect on air quality, leading to an increase in air pollution. Thus, companies attracted by tax incentives may not meet rigorous environmental standards. These results highlight the importance for policymakers to balance economic incentives with environmental protection in ECOWAS. Attractive tax policies can stimulate investment, but they must be designed in a way that encourages environmentally friendly practices, thereby helping to improve air quality in the region.

Spatial heterogeneity of factors influencing forest fires size in northern Mexico

In Mexico, forest fires are strongly influenced by environmental, topographic, and anthropogenic factors. A government-based database covering the period 2000-2011 was used to analyze the spatial heterogeneity of the factors influencing forest fire size in the state of Durango, Mexico. Ordinary least squares and geographically weighted regression models were fit to identify the main factors as well as their spatial influence on fire size. Results indicate that fire size is greatly affected by distance to roads, distance to towns, precipitation, temperature, and a population gravity index. The geographically weighted model was better than the ordinary least squares model. The improvement of the former is due to the influence of factors that were found to be non-stationary. These results suggest that geographic location determines the influence of a factor on fire size. While the models can be greatly improved with additional information, the study suggests the need to adopt fire management policies to more efficiently reduce the effect of anthropogenic factors. These policies may include more training for landowners who use fire for clearing, closure of roads, application of thinning, prescribed burning, and fire breaks in perimeters adjacent to roads.

Mapping determinants of rural poverty in Guangxi–a less developed region of China

Rapid urbanization in China has led to an increasing imbalance in regional development.The Guangxi Zhuang Autonomous Region,a less developed border region with unique cultural diversity,has a relatively large population(4.52 million people in 2015)under the poverty line,according to the national standard of poverty.China has launched a national campaign to reduce poverty using a wide range of new development policies and large-scale investment.However,there have been few studies on the determinants of poverty at the county level across a province.This paper aims to explore the spatial and social differences related to poverty among 109 counties by considering the spatial heterogeneity of poverty determinants.Spatial statistical models revealed that slope(Slp),GDP per capita(GDPP),the ethnic minority population ratio(EMPR),medical and technical personnel of healthcare institutions(MTP)and illiteracy rate(IR)significantly affect the patterns of the poverty rate,with a high adjusted R2(0.67),while the poverty rate affects GDPP,IR,MTP and EMPR;i.e.,the effects are interactional.Furthermore,the IR is significantly affected by the provision of schools and transportation conditions.Among these determinants,social factors may be key.The spatial patterns of these relationships demonstrate remarkable variation across the province and between minor and major groups.This quantitative evidence is enhanced by indepth interviews with selected groups.These results are expected to be useful for the anti-poverty project in Guangxi.

Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model

The paper aims at exploring the relationship between housing resale prices and neighborhood features in Zhuhai,as well as structure and location characteristics.Thirteen neighborhood features are collected to analyze their influence on average community-level apartment resale prices in 2018.Six neighbor-hood features,structural and location characteristics,are selected according to their statistical significance and multi-collinearity test results from an OLS model.Regression analysis is performed by OLS,GWR,and MGWR to compare their per-formance in housing price research at community level.The comparison of the three models also demonstrates that the GWR(66%)and MGWR(68%)models perform much better than OLS model(52%).MGWR is not significantly different from GWR in areas with few sample points,and the optimal bandwidth at different spatial scales is hard to be captured in a city-level study area.The regression parameter indicates that building age is the most important factor among all influen-cing factors.Proximity to schools and factories have positive and negative significant effects on housing resale prices,respectively.The spatial pattern of neighborhood features is also detected at town level.GWR and MGWR models accurately demonstrate local spatial heterogeneity of the housing resale market,which provides better results than the traditional OLS model in the goodness of fit and parameter estimates when spatial dependency is present.The results provide references for local planning departments,helping to reveal the compli-cated relationship and spatial patterns between housing price and determinants over space.

Predictive modelling of COVID-19 confirmed cases in Nigeria

The coronavirus outbreak is the most notable world crisis since the SecondWorldWar.The pandemic that originated from Wuhan,China in late 2019 has affected all the nations of the world and triggered a global economic crisis whose impact will be felt for years to come.This necessitates the need to monitor and predict COVID-19 prevalence for adequate control.The linear regression models are prominent tools in predicting the impact of certain factors on COVID-19 outbreak and taking the necessary measures to respond to this crisis.The data was extracted from the NCDC website and spanned from March 31,2020 to May 29,2020.In this study,we adopted the ordinary least squares estimator to measure the impact of travelling history and contacts on the spread of COVID-19 in Nigeria and made a prediction.The model was conducted before and after travel restriction was enforced by the Federal government of Nigeria.The fitted model fitted well to the dataset and was free of any violation based on the diagnostic checks conducted.The results show that the government made a right decision in enforcing travelling restriction because we observed that travelling history and contacts made increases the chances of people being infected with COVID-19 by 85%and 88%respectively.This prediction of COVID-19 shows that the government should ensure that most travelling agency should have better precautions and preparations in place before re-opening.

Effect of time-of-day and day-of-the-week on congestion duration and breakdown:A case study at a bottleneck in Salem,NH

This work uses regression models to analyze two characteristics of recurrent congestion： breakdown, the transition from freely flowing conditions to a congested state, and duration, the time between the onset and clearance of recurrent congestion. First, we apply a binary logistic regression model where a continuous measurement for traffic flow and a dichoto- mous categorical variable for time-of-day （AM- or PM-rush hours） is used to predict the probability of breakdown. Second, we apply an ordinary least squares regression model where categorical variables for time-of-day （AM- or PM-rush hours） and day-of-the-week （Monday-Thursday or Friday） are used to predict recurrent congestion duration. Models are fitted to data collected from a bottleneck on 1-93 in Salem, NH, over a period of 9 months. Results from the breakdown model, predict probabilities of recurrent congestion, are consistent with observed traffic and illustrate an upshift in breakdown probabilities between the AM- and PM-rush periods. Results from the regression model for congestion duration reveal the presences of significant interaction between time-of-day and day-of-the-week. Thus, the effect of time-of-day on congestion duration depends on the day-of-the-week. This work provides a simplification of recurrent congestion and recovery, very noisy processes. Simplification, conveying complex relationships with simple statistical summaries-facts, is a practical and powerful tool for traffic administrators to use in the decision-making process.